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Tolt, Gustav. "Fuzzy similarity-based image processing /." Örebro : Örebro universitetsbibliotek, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-97.
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Chandran, Gautam David. "The development of a fuzzy semantic sentence similarity measure." Thesis, Manchester Metropolitan University, 2013. http://e-space.mmu.ac.uk/617190/.
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A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent the effect perception based (fuzzy) words, which are commonly used in natural language, have on sentence similarity. This research project developed a new sentence similarity measure to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is a novel ontology-based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the fuzzy words contained within them and from that a new fuzzy ontology was created. Using these relationships allows for the creation of a new ontology-based fuzzy semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. In order to evaluate FAST, two new test datasets were created through the use of questionnaire based human experimentation. This involved the generation of a robust methodology for creating usable fuzzy datasets (including an automated method that was used to create one of the two fuzzy datasets). FAST was evaluated through experiments conducted using the new fuzzy datasets. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures demonstrating its success in representing the similarity between pairs of sentences containing fuzzy words.
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Bashon, Yasmina M. "Contributions to fuzzy object comparison and applications. Similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.
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This thesis makes an original contribution to knowledge in the fi eld
of data objects' comparison where the objects are described by attributes
of fuzzy or heterogeneous (numeric and symbolic) data types.
Many real world database systems and applications require information
management components that provide support for managing
such imperfect and heterogeneous data objects. For example,
with new online information made available from various sources, in
semi-structured, structured or unstructured representations, new information
usage and search algorithms must consider where such data
collections may contain objects/records with di fferent types of data:
fuzzy, numerical and categorical for the same attributes.
New approaches of similarity have been presented in this research to
support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity
measures presented in this thesis, to handle the vagueness (fuzzy data
type) within data objects. A framework of new and unif ied similarity
measures for comparing heterogeneous objects described by numerical,
categorical and fuzzy attributes has also been introduced.
Examples are used to illustrate, compare and discuss the applications
and e fficiency of the proposed approaches to heterogeneous data comparison.Libyan Embassy
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Bashon, Yasmina Massoud. "Contributions to fuzzy object comparison and applications : similarity measures for fuzzy and heterogeneous data and their applications." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6305.
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This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
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IONESCU, MIRCEA MARIAN. "ADAPTIVE MEASURES OF SIMILARITY - FUZZY HAMMING DISTANCE - AND ITS APPLICATIONS TO PATTERN RECOGNITION PROBLEMS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163708478.
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McCulloch, Josie C. "Novel methods of measuring the similarity and distance between complex fuzzy sets." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33401/.
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This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, making relative comparisons between data-driven fuzzy sets can be challenging. For example, when data sets are ambiguous or contradictory, then the fuzzy set models often become non-normal or non-convex, making them difficult to compare. This thesis presents methods of comparing data that may be represented by such (complex) non-normal or non-convex fuzzy sets. The developed approaches for calculating relative comparisons also enable fusing methods of measuring similarity and distance between fuzzy sets. By using multiple methods, more meaningful comparisons of fuzzy sets are possible. Whereas if only a single type of measure is used, ambiguous results are more likely to occur. This thesis provides a series of advances around the measuring of similarity and distance. Based on them, novel applications are possible, such as personalised and crowd-driven product recommendations. To demonstrate the value of the proposed methods, a recommendation system is developed that enables a person to describe their desired product in relation to one or more other known products. Relative comparisons are then used to find and recommend something that matches a person's subjective preferences. Demonstrations illustrate that the proposed method is useful for comparing complex, non-normal and non-convex fuzzy sets. In addition, the recommendation system is effective at using this approach to find products that match a given query.
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Wagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.
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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
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Wagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.
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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
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Onescu, Mircea Marian. "Adaptive measures of similarity - fuzzy hamming distance - and its applications to pattern recognition problems." Cincinnati, Ohio : University of Cincinnati, 2006. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1163708478.
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Thesis (Ph. D.)--University of Cincinnati, 2006..Title from electronic thesis title page (viewed Jan.27, 2007). Includes abstract. Keywords: Fuzzy Hamming Distance, artificial intelligence, fuzzy, image retrieval system Includes bibliographical references.
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Lo, Yi-Chen. "Detection of gas/odor based on quartz crystal microbalance sensors and fuzzy similarity measure." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Garcia, Ian. "Eliminating Redundant and Less-informative RSS News Articles Based on Word Similarity and A Fuzzy Equivalence Relation." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1688.pdf.
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Azzeh, Mohammad Y. A. "Analogy-based software project effort estimation : contributions to projects similarity measurement, attribute selection and attribute weighting algorithms for analogy-based effort estimation." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4442.
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Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners' acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings.
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Davault, Julius Mack III. "Resolving Quasi-Synonym Relationships in Automatic Thesaurus Construction using Fuzzy Rough Sets and an Inverse Term Frequency Similarity Function." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/129.
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One of the problems associated with automatic thesaurus construction is with determining the semantic relationship between word pairs. Quasi-synonyms provide a type of equivalence relationship: words are similar only for purposes of information retrieval. Determining such relationships in a thesaurus is hard to achieve automatically. The term vector space model and an inverse term frequency similarity function can provide a way to automatically determine the similarity between words in thesaurus. A thesaurus constructed using this method can also improve precision and recall in information retrieval, when the thesaurus is constructed in conjunction with fuzzy rough set algorithms and used with tight upper approximation query expansion. This dissertation presents a method that combines fuzzy rough sets and a word weighting and inverse term frequency similarity function as a technique for automatic thesaurus construction.
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Tocatlidou, Athena. "The use of evidential support logic and a new similarity measurement for fuzzy sets to model the decision making process." Thesis, University of Bristol, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337159.
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Bergman, John. "Efficient fuzzy type-ahead search on big data using a ranked trie data structure." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-145029.
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The efficiency of modern search engines depends on how well they present typo-corrected results to a user while typing. So-called fuzzy type-ahead search combines fuzzy string matching and search-as-you-type functionality, and creates a powerful tool for exploring indexed data. Current fuzzy type-ahead search algorithms work well on small data sets, but for big data of social networking services such as Facebook, e-commerce sites such as Amazon, or media streaming services such as YouTube, responsive fuzzy type-ahead search remains a great challenge. This thesis describes a method that enables responsive type-ahead search combined with fuzzy string matching on big data by keeping the search time optimal for human interaction at the expense of lower accuracy for less popular records when a query contains typos. This makes the method effective for e-commerce and media services where the popularity of search terms is a result of human behaviour and thus often follow a power-law distribution.Effektiviteten hos moderna sökmotorer beror på hur väl de presenterar rättstavade resultat för en användare medan en sökning skrivs. Så kallad fuzzy type-ahead sök kombinerar approximativ strängmatchning och sök-medan-du-skriver funktionalitet, vilket skapar ett kraftfullt verktyg för att utforska data. Dagens algoritmer för fuzzy type-ahead sök fungerar väl för små mängder data, men för data i storleksordningen “big data” från t.ex sociala nätverkstjänster så som Facebook, e-handelssidor så som Amazon, eller media tjänster så som YouTube, är en responsiv fuzzy type-ahead sök ännu en stor utmaning. Denna avhandling beskriver en metod som möjliggör responsiv type-ahead sök kombinerat med approximativ strängmatchning för big data genom att hålla söktiden optimal för mänsklig interaktion på bekostnad av lägre precision för mindre populär information när en sök-förfrågan innehåller felstavningar. Detta gör metoden effektiv för e-handel och mediatjänster där populariteten av sök-termer är ett resultat av mänskligt beteende vilket ofta följer en potens-lag distribution.
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Goyal, Vivek. "A Recommendation System Based on Multiple Databases." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.
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Croft, David. "Semi-automated co-reference identification in digital humanities collections." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10491.
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Locating specific information within museum collections represents a significant challenge for collection users. Even when the collections and catalogues exist in a searchable digital format, formatting differences and the imprecise nature of the information to be searched mean that information can be recorded in a large number of different ways. This variation exists not just between different collections, but also within individual ones. This means that traditional information retrieval techniques are badly suited to the challenges of locating particular information in digital humanities collections and searching, therefore, takes an excessive amount of time and resources. This thesis focuses on a particular search problem, that of co-reference identification. This is the process of identifying when the same real world item is recorded in multiple digital locations. In this thesis, a real world example of a co-reference identification problem for digital humanities collections is identified and explored. In particular the time consuming nature of identifying co-referent records. In order to address the identified problem, this thesis presents a novel method for co-reference identification between digitised records in humanities collections. Whilst the specific focus of this thesis is co-reference identification, elements of the method described also have applications for general information retrieval. The new co-reference method uses elements from a broad range of areas including; query expansion, co-reference identification, short text semantic similarity and fuzzy logic. The new method was tested against real world collections information, the results of which suggest that, in terms of the quality of the co-referent matches found, the new co-reference identification method is at least as effective as a manual search. The number of co-referent matches found however, is higher using the new method. The approach presented here is capable of searching collections stored using differing metadata schemas. More significantly, the approach is capable of identifying potential co-reference matches despite the highly heterogeneous and syntax independent nature of the Gallery, Library Archive and Museum (GLAM) search space and the photo-history domain in particular. The most significant benefit of the new method is, however, that it requires comparatively little manual intervention. A co-reference search using it has, therefore, significantly lower person hour requirements than a manually conducted search. In addition to the overall co-reference identification method, this thesis also presents: • A novel and computationally lightweight short text semantic similarity metric. This new metric has a significantly higher throughput than the current prominent techniques but a negligible drop in accuracy. • A novel method for comparing photographic processes in the presence of variable terminology and inaccurate field information. This is the first computational approach to do so.
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Ayres, Rodrigo Moura Juvenil. "Mineração de regras de associação generalizadas utilizando ontologias fuzzy e similaridade baseada em contexto." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/503.
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Previous issue date: 2012-08-08Financiadora de Estudos e Projetos
The mining association rules are an important task in data mining. Traditional algorithms of mining association rules are based only on the database items, providing a very specific knowledge. This specificity may not be advantageous, because the users normally need more general, interesting and understandable knowledge. In this sense, there are approaches working in order to obtain association rules with items belonging to any level of a taxonomic structure. In the crisp contexts taxonomies are used in different steps of the mining process. When the objective is the generalization they are used, mainly, in the pre-processing or post-processing stages. On the other hand, in the fuzzy context, fuzzy taxonomies are used, mainly, in the pre-processing step, during the generating extended transactions. A great problem of these transactions is related to the huge amount of candidates and rules. Beyond that, the inclusion of ancestors ends up generating redundancy problems. Besides, it is possible to see that many works have directed efforts for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have been proposed for explore new steps of mining process. In this sense, this paper proposes the Context FOntGAR algorithm, a new algorithm for mining generalized association rules under all levels of fuzzy ontologies composed by specialization/generalization degrees varying in the interval [0,1]. In order to obtain more semantic enrichment, the rules may be composed by similarity relations, which are represented at the fuzzy ontologies in different contexts. In this work the generalization is done during the post-processing step. Other relevant points of this paper are the specification of a new approach of generalization; including a new grouping rules treatment, and a new and efficient way for calculating both support and confidence of generalized rules.
Algoritmos tradicionais de associação se caracterizam por utilizar apenas itens contidos na base de dados, proporcionando um conhecimento muito específico. No entanto, essa especificidade nem sempre é vantajosa, pois normalmente os usuários finais necessitam de padrões mais gerais, e de fácil compreensão. Nesse sentido, existem abordagens que não se limitam somente aos itens da base, e trabalham com o objetivo de minerar regras (generalizadas) com itens presentes em qualquer nível de estruturas taxonômicas. Taxonomias podem ser utilizadas em diferentes etapas do processo de mineração. A literatura mostra que, em contextos crisp, essas estruturas são utilizadas tanto em etapa de pré-processamento, quanto em etapa de pós-processamento, e que em domínios fuzzy, a utilização ocorre somente na etapa de pré-processamento, durante a geração de transações estendidas. Além do viés de utilização de transações estendidas, que podem levar a geração de um volume de regras superior ao caso tradicional, é possível notar que, em domínios nebulosos, as pesquisas dão enfoque apenas à mineração de regras fuzzy, deixando de lado a exploração de diferentes graus de especialização/generalização em taxonomias. Nesse sentido, este trabalho propõem o algoritmo FOntGAR, um novo algoritmo para mineração de regras de associação generalizadas com itens presentes em qualquer nível de ontologias compostas por graus de especialização/generalização variando no intervalo [0,1] (ontologias de conceitos fuzzy), em etapa de pós-processamento. Objetivando obter maior enriquecimento semântico, as regras geradas pelo algoritmo também podem possuir relações de similaridade, de acordo com contextos pré-definidos. Outros pontos relevantes são a especificação de uma nova abordagem de generalização (incluindo um novo tratamento de agrupamento das regras), e um novo e eficiente método para calcular o suporte estendido das regras generalizadas durante a etapa mencionada.
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Medeiros, Anderson Vinicius de. "Modelagem de sistemas dinamicos não lineares utilizando sistemas fuzzy, algoritmos geneticos e funções de base ortonormal." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261859.
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Orientadores: Wagner Caradori do Amaral, Ricardo Jose Gabrielli Barreto CampelloDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Esta dissertação apresenta uma metodologia para a geração e otimização de modelos fuzzy Takagi-Sugeno (TS) com Funções de Base Ortonormal (FBO) para sistemas dinâmicos não lineares utilizando um algoritmo genético. Funções de base ortonormal têm sido utilizadas por proporcionarem aos modelos propriedades como ausência de recursão da saída e possibilidade de se alcançar uma razoável capacidade de representação com poucos parâmetros. Modelos fuzzy TS agregam a essas propriedades as características de interpretabilidade e facilidade de representação do conhecimento. Enfim, os algoritmos genéticos se apresentam como um método bem estabelecido na literatura na tarefa de sintonia de parâmetros de modelos fuzzy TS. Diante disso, desenvolveu-se um algoritmo genético para a otimização de duas arquiteturas, o modelo fuzzy TS FBO e sua extensão, o modelo fuzzy TS FBO Generalizado. Foram analisados modelos locais lineares e não lineares nos conseqüentes das regras fuzzy, assim como a diferença entre a estimação local e a global (utilizando o estimador de mínimos quadrados) dos parâmetros desses modelos locais. No algoritmo genético, cada arquitetura contou com uma representação cromossômica específica. Elaborou-se para ambas uma função de fitness baseada no critério de Akaike. Em relação aos operadores de reprodução, no operador de crossover aritmético foi introduzida uma alteração para a manutenção da diversidade da população e no operador de mutação gaussiana adotou-se uma distribuição variável ao longo das gerações e diferenciada para cada gene. Introduziu-se ainda um método de simplificação de soluções através de medidas de similaridade para a primeira arquitetura citada. A metodologia foi avaliada na tarefa de modelagem de dois sistemas dinâmicos não lineares: um processo de polimerização e um levitador magnético
Abstract: This work introduces a methodology for the generation and optimization of Takagi-Sugeno (TS) fuzzy models with Orthonormal Basis Functions (OBF) for nonlinear dynamic systems based on a genetic algorithm. Orthonormal basis functions have been used because they provide models with properties like absence of output feedback and the possibility to reach a reasonable approximation capability with just a few parameters. TS fuzzy models aggregate to these properties the characteristics of interpretability and easiness to knowledge representation in a linguistic manner. Genetic algorithms appear as a well-established method for tuning parameters of TS fuzzy models. In this context, it was developed a genetic algorithm for the optimization of two architectures, the OBF TS fuzzy model and its extension, the Generalized OBF TS fuzzy model. Local linear and nonlinear models in the consequent of the fuzzy rules were analyzed, as well as the difference between local and global estimation (using least squares estimation) of the parameters of these local models. Each architecture had a specific chromosome representation in the genetic algorithm. It was developed a fitness function based on the Akaike information criterion. With respect to the genetic operators, the arithmetic crossover was modified in order to maintain the population diversity and the Gaussian mutation had its distribution varied along the generations and differentiated for each gene. Besides, it was used, in the first architecture presented, a method for simplifying the solutions by using similarity measures. The whole methodology was evaluated in modeling two nonlinear dynamic systems, a polymerization process and a magnetic levitator
Mestrado
Automação
Mestre em Engenharia Elétrica
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Clara, i. Lloret Narcís. "Estudi de mètodes de classificació borrosa i la seva aplicació a l'agrupació de zones geogràfiques en base a diverses característiques incertes." Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7755.
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Aquesta memòria està estructurada en sis capítols amb l'objectiu final de fonamentar i desenvolupar les eines matemàtiques necessàries per a la classificació de conjunts de subconjuntsborrosos. El nucli teòric del treball el formen els capítols 3, 4 i 5; els dos primers són dos capítols de caire més general, i l'últim és una aplicació dels anteriors a la classificació dels
països de la Unió Europea en funció de determinades característiques borroses.
En el capítol 1 s'analitzen les diferents connectives borroses posant una especial atenció en aquells aspectes que en altres capítols tindran una aplicació específica. És per aquest motiu que s'estudien les ordenacions de famílies de t-normes, donada la seva importància en la transitivitat de les relacions borroses. La
verificació del principi del terç exclòs és necessària per assegurar que un conjunt significatiu de mesures borroses generalitzades, introduïdes en el capítol 3, siguin reflexives.
Estudiem per a quines t-normes es verifica aquesta propietat i introduïm un nou conjunt de t-normes que verifiquen aquest principi.
En el capítol 2 es fa un recorregut general per les relacions borroses centrant-nos en l'estudi de la clausura transitiva per a qualsevol t-norma, el càlcul de la qual és en molts casos
fonamental per portar a terme el procés de classificació. Al final del capítol s'exposa un procediment pràctic per al càlcul d'una
relació borrosa amb l'ajuda d'experts i de sèries estadístiques.
El capítol 3 és un monogràfic sobre mesures borroses. El primer objectiu és relacionar les mesures (o distàncies) usualment utilitzades en les aplicacions borroses amb les mesures
conjuntistes crisp. Es tracta d'un enfocament diferent del tradicional enfocament geomètric. El principal resultat és la introducció d'una família parametritzada de mesures que verifiquen
unes propietats de caràcter conjuntista prou satisfactòries.
L'estudi de la verificació del principi del terç exclòs té aquí la seva aplicació sobre la reflexivitat d'aquestes mesures, que són
estudiades amb una certa profunditat en alguns casos particulars.
El capítol 4 és, d'entrada, un repàs dels principals resultats i mètodes borrosos per a la classificació dels elements d'un mateix
conjunt de subconjunts borrosos. És aquí on s'apliquen els resultats sobre les ordenacions de les famílies de t-normes i t-conormes estudiades en el capítol 1. S'introdueix un nou mètode
de clusterització, canviant la matriu de la relació borrosa cada vegada que s'obté un nou clúster. Aquest mètode permet homogeneïtzar la metodologia del càlcul de la relació borrosa amb
el mètode de clusterització.
El capítol 5 tracta sobre l'agrupació d'objectes de diferent naturalesa; és a dir, subconjunts borrosos que pertanyen a diferents conjunts. Aquesta teoria ja ha estat desenvolupada en el
cas binari; aquí, el que es presenta és la seva generalització al cas n-ari. Més endavant s'estudien certs aspectes de les projeccions de la relació sobre un cert espai i el recíproc,
l'estudi de cilindres de relacions predeterminades. Una aplicació sobre l'agrupació de les comarques gironines en funció de certes
variables borroses es presenta al final del capítol.
L'últim capítol és eminentment pràctic, ja que s'aplica allò estudiat principalment en els capítols 3 i 4 a la classificació dels països de la Unió Europea en funció de determinades
característiques borroses. Per tal de fer previsions per a anys venidors s'han utilitzat sèries temporals i xarxes neuronals.
S'han emprat diverses mesures i mètodes de clusterització per tal de poder comparar els diversos dendogrames que resulten del procés
de clusterització.
Finalment, als annexos es poden consultar les sèries estadístiques utilitzades, la seva extrapolació, els càlculs per a la construcció de les matrius de les relacions borroses, les matrius
de mesura i les seves clausures.
This thesis is organized in six chapters with the final goal to found and explain the mathematical set of tools necessary to classify sets of fuzzy sets. The theoretic kernel is made by the chapters 3, 4 and 5; the first and second are more generals and the last one is an aplication of the precedent to make a classification of the union european countries in function of some vague attibutes.
In the first chapter we analize the different fuzzy logic connectives making a special attention those aspects which will have a specific application in other chapters. Is for this reason that we study the order of families of t-norms, given its importance in the transivity of fuzzy relations. The verification of the third excluded principle is necessary to ensure that a significant set of generalized fuzzy measures, introduced in the chapter 3, were reflexive. We study for which t-norms is verified this property and we introduce a new set of t-norms which verify this principle.
In the second chapter we study in a general way the fuzzy relations making a special attention in the transivity closure for any t-norm, its calculus is in a lot of cases basic to make the classification process. At the end of this chapter we describe a practical method to find a fuzzy relation with the help of experts and statistical series.
The third chapter is a monographic about fuzzy measures. The first goal is to relate the measures (or distances) usually used in the fuzzy applications with the crisp measures. The question is to change the traditional geometrical point of view for another absolutely fuzzy. The first result is the introduction of a parametrized family of measures that verify a set of properties enough satisfactories. The study of the third exclude principle has here its application about the reflexivity of these measures which are studied with certain profundity in some particular cases.
The fourth chapter is, at the beginning, a review of the main results and fuzzy methods for the classification of elements of a same set of fuzzy sets. Is now where we apply the results of orders for t-norms and t-conorms studied in the first chapter. We introduce a new method of fuzzy clustering, changing the fuzzy relation matrix each time that we obtain a new cluster. This method permit to homogenize the methodology of the calculus of the fuzzy relation with the clustering method.
The fifth chapter is about the objects association of different nature; that is, fuzzy subsets that belong to different sets. This theory already has been developed in the binary case; here, we submit its generalization for the n dimensional case. Later, we study certain aspects of the fuzzy relation projection on a certain space and the reciprocal, the cilindrical extensions. An application about grouping regions of Girona in function of some uncertain attibutes finish the chapter.
The last chapter is eminently applied, because we apply that studied in the 3 and 4 chapters to classify the union european countries in function of some fuzzy attributes. To do forecasts for coming years we have used time series and neural networks. We have used several measures and clustering methods in order to compare the dendograms that result of the clustering process.
Finally, in the suplements we can consult the used time series, its extrapolation, the calculus to construct the fuzzy relations, the measure matrixs and its closures.
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Phan, Thi-Thu-Hong. "Elastic matching for classification and modelisation of incomplete time series." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0483/document.
Full textAbstract:
Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au VietnamMissing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam
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Bacak, Hikmet Ozge. "Decision Making System Algorithm On Menopause Data Set." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12612471/index.pdf.
Full textAbstract:
Multiple-centered clustering method and decision making system algorithm on menopause data set depending on multiple-centered clustering are described in this study. This method consists of two stages. At the first stage, fuzzy C-means (FCM) clustering algorithm is applied on the data set under consideration with a high number of cluster centers. As the output of FCM, cluster centers and membership function values for each data member is calculated. At the second stage, original cluster centers obtained in the first stage are merged till the new numbers of clusters are reached. Merging process relies upon a &ldquosimilarity measure&rdquo
between clusters defined in the thesis. During the merging process, the cluster center coordinates do not change but the data members in these clusters are merged in a new cluster. As the output of this method, therefore, one obtains clusters which include many cluster centers. In the final part of this study, an application of the clustering algorithms &ndash
including the multiple centered clustering method &ndash
a decision making system is constructed using a special data on menopause treatment. The decisions are based on the clusterings created by the algorithms already discussed in the previous chapters of the thesis. A verification of the decision making system / v decision aid system is done by a team of experts from the Department of Department of Obstetrics and Gynecology of Hacettepe University under the guidance of Prof. Sinan Beksaç
.
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Escovar, Eduardo Luís Garcia. "Algoritmo SSDM para a mineração de dados semanticamente similares." Universidade Federal de São Carlos, 2004. https://repositorio.ufscar.br/handle/ufscar/495.
Full textAbstract:
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Previous issue date: 2004-05-28Financiadora de Estudos e Projetos
The SSDM algorithm, created to allow semantically similar data mining, is presented in this work. Using fuzzy logic concepts, this algorithm analyzes the similarity grade between items, considering it if it is greater than a user-defined parameter. When this occurs, fuzzy associations between items are established, and are expressed in the association rules obtained. Therefore, besides associations discovered by conventional algorithms, SSDM also discovers semantic associations, showing them together with the other rules obtained. To do that, strategies are defined to discover these associations and calculate the support and the confidence of the rules where they appear.
Neste trabalho é apresentado o algoritmo SSDM, criado para permitir a mineração de dados semanticamente similares. Usando conceitos de lógica nebulosa, esse algoritmo analisa o grau de similaridade entre os itens, e o considera caso ele seja maior do que um parâmetro definido pelo usuário. Quando isso ocorre, são estabelecidas associações nebulosas entre os itens, que são expressas nas regras de associação obtidas. Assim, além das associações descobertas por algoritmos convencionais, o SSDM também descobre associações semânticas, e as exibe junto às demais regras obtidas. Para isso, são definidas estratégias para descobrir essas associações e para calcular o suporte e a confiança das regras onde elas aparecem.
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Casanova, Anderson Araújo. "MINERAÇÃO DE DADOS: ALGORITMO DA CONFIANÇA INVERSA." Universidade Federal do Maranhão, 2005. http://tedebc.ufma.br:8080/jspui/handle/tede/373.
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Previous issue date: 2005-06-28This work presents studies that culminated in the development of a data mining algorithm that extracts knowledge in a more efficient way and allows for a better use of the collected information. Decisions based on imprecise information and a lack of criteria can cause the relatively few resources available to be poorly applied, burdening taxpayers and consequently the state. This much-needed information which allows for the fairest and most efficient application of available resources and which would facilitate the work of the users as well as those who render the services should be based upon consideration of the great variety of established criteria. The making of a decision should be based upon the evaluation of the most varied types of data and be analyzed by specialists who can judge which are true needs, so that the criteria for the search of knowledge may be defined. The Algorithm of Inverse Confidence - ACI accomplishes data mining using the technique of association rules, and it proposes a new measure that enlarges the dimension of extracted information through five fixed rules. ACI also classifies and associates items, using the concept of the fuzzy logic, through parameters established by the user. ACI was applied in the surgical center of HUUFMA - Academical Hospital of the Federal University of Maranhão - envisioning the extraction of knowledge (standards).
Este trabalho apresenta estudos que culminaram no desenvolvimento de um algoritmo de mineração de dados que, faz extração de conhecimento e que possibilita um melhor aproveitamento das informações coletadas. Decisões baseadas em informações imprecisas e com falta de critérios podem fazer com que recursos, de qualquer tipo, sejam mal aplicados. A informação necessária que tornem a aplicação dos recursos mais justa e eficiente, e que facilitem o trabalho tanto dos usuários de um determinado serviço quanto aos que prestam o serviço, devem ser baseadas considerando a grande variedade de critérios estabelecidos. A tomada de decisão deve ser com base na avaliação dos mais variados tipa de dados e analisada por especialistas que julguem quais as necessidades, para que os critérios de busca do conhecimento sejam definidos. O Algoritmo da Confiança Inversa ACI realiza mineração de dados utilizando a técnica de regras de associação e propõe uma nova medida que amplia a dimensão das informações extraídas através de cinco regras fixas. O ACI também classifica e associa itens similares, utilizando o conceito da lógica nebulosa (fuzzy logic), através de parâmetro estabelecido pelo usuário. O ACI foi aplicado no centro cirúrgico do HUUFMA Hospital Universitário da Universidade Federal do Maranhão visando à extração de conhecimento (padrões).
25
Yun-Lun, Tsai, and 蔡昀倫. "New Fuzzy-Number Similarity Measures for Fuzzy Clustering Problems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/86716547251835793537.
Full textAbstract:
碩士國立聯合大學
資訊管理學系碩士班
102
This study propose two methods for measuring the degree of similarity with generalized fuzzy numbers and interval-valued fuzzy numbers based on mean mapping distance ratio and mean absolute deviation. Some properties of the methods are demonstrated, then 56 sets of generalized fuzzy numbers and 25 sets of interval-valued fuzzy numbers are adopted to compare the proposed methods with the existing methods. The results indicated that the proposed methods are better than existing methods. Furthermore, we propose a fuzzy number clustering method using the proposed methods of similarity measure, and then we analyzed four literatures and compared the proposed clustering method with the existing methods. Comparative results indicated that the proposed method can overcome the drawbacks of the existing methods. A numerical example is demonstrated the new mechanism.
26
WEN, CHU-CHUN, and 溫筑均. "New Fuzzy-number Similarity Measure for Fuzzy Classification Problem." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r9m27j.
Full textAbstract:
碩士國立聯合大學
資訊管理學系碩士班
106
The similarity measure of generalized fuzzy numbers has been widely used in data mining, machine learning, and other fields in recent years. This study proposes a method for measuring the degree of similarity between Generalized Fuzzy Numbers based on the concept of Quadratic Mean and Bevel-projection Difference Averaging operator (BPDA). At the same time, the three characteristics of this method are also proved, and 48 sets of generalized fuzzy numbers are used to compare the proposed method with the existing methods. The comparison results show that the method proposed in this study is superior to existing methods. Then, we propose a classification algorithm based on the fuzzy-number similarity measure. We also present a method to measure the similarity between the fuzzy numbers with different left and right heights for the fuzzy classification algorithm. Finally, the actual case validation is conducted through the Iris data, and the results are compared with the past classification methods.
27
Hsiao, Chien-Lan, and 蕭建蘭. "Similarity Measures in Fuzzy Regression Models." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/74145993398701467870.
Full text
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Guan-JyunLong and 龍冠君. "A Fuzzy Similarity-based Forecasting Model for Fuzzy Time Series." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/45616496209057685552.
Full textAbstract:
碩士國立成功大學
資訊管理研究所
98
In our daily life, vague and incomplete data described as linguistic variables massively exists in various areas. Therefore, fuzzy time series forecasting plays an important role for uncertain situations. Various forecasting models have been proposed with an emphasis on improving forecasting accuracy or reducing computation cost. Generally speaking, the framework of a fuzzy time series forecasting model is constructed of four major steps: (1) determining and partitioning the universe of discourse, (2) defining the fuzzy sets on the universe of discourse and fuzzifying the time series, (3) constructing fuzzy logical relationships existing in the fuzzified time series, and (4) forecasting and defuzzifying of its outputs. However, most of the researches derive the fuzzy logical relationships in step (3) only using the exact-match IF-THEN rules. Their forecasting models ignore the fuzzy character in forecasting step, and have two shortcomings in the following: (1) If the data is not enough to generate sufficient fuzzy logical rules, the low rate of rule matching may occur in forecasting process. (2) If the order of a fuzzy logical relationship is quite high, the previous models will become hardly to find the matching rules in the forecasting process. Therefore, in this study, we propose the fuzzy similarity-based forecasting model for fuzzy time series to solve the two shortcomings and raise the forecasting accuracy.
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Kang, Li-Chun, and 康麗君. "Similarity-Relationship Based Fuzzy Relational Database Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/7u5h52.
Full textAbstract:
碩士中原大學
數學研究所
92
The system of fuzzy database is the theory that uses fuzzy mathematics to expand the traditional relation database. Its goal is to store imprecise information and to deal with inquiries of inexact information. In this dissertation, we introduce the essential concepts of the fuzzy set theory and relational database. Fuzzy theory was proposed by L. A. Zedah in1965. Some scholars did not apply it to database management system until 1980.To represent imprecise, uncertain, and incomplete information, we discuss the similarity-based fuzzy databases. This model extends the definition of traditional relational model in that attribute values. Use the similarity relation to replace equivalence relation to compare with the attribute values. In addition, the repeated tuples have to be combined. Similarity relation is applied when the domain is a finite set. In this paper, we work on how to expand the definition of the domain to infinite sets, and use and to define the fuzzy numbers of similarity relation. Although varieties of similarity relation were proposed, now we aim at detailed description of important theorems. We focus on how to make an inquiry about the fuzzy semantics in our daily lives, and find out the similarity therein. Then, we conduct transactions or contrast them. Finally, we bring up concrete examples to introduce how to apply the fuzzy database in reality.
30
HUANG, GUAN-YING, and 黃冠熒. "Application of Novel Fuzzy Numbers Similarity Measure in Fuzzy-numbers Classification." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/988trk.
Full textAbstract:
碩士國立聯合大學
資訊管理學系碩士班
105
This study proposes a method for measuring the degree of similarity with generalized fuzzy numbers based on quadratic mean. Some properties of the method are demonstrated, then 56 sets of generalized fuzzy numbers are adopted to compare the proposed methods with the existing methods. The results indicated that the proposed methods are better than existing methods. Furthermore, we propose a fuzzy number classification method using the proposed methods of similarity measure, and then we analyze the literature and compare the proposed classification method with the existing method. Comparative results indicate that the proposed method can overcome the drawbacks of the existing method. A numerical example is demonstrated the new algorithm.
31
Wang, ZhiYong, and 王智永. "Design New Fuzzy-Number Similarity Measures and Fuzzy-Number Clustering Method." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/41112788594070593554.
Full textAbstract:
碩士國立聯合大學
資訊管理學系碩士班
100
This study proposes new methods based on map distance to measure the degree of similarity between generalized and interval-valued fuzzy numbers. Some properties of the proposed similarity measures are demonstrated here. There are 50 sets of generalized fuzzy numbers and 21 sets of interval-valued fuzzy numbers are adopted to compare the proposed methods with some existing similarity measures for proving the proposed similarity measures are better than the existing methods. Furthermore, the proposed similarity measure is used to deal with fuzzy-number cluster problems. We present a new method for handling the fuzzy clustering problems of which the characteristic values and weights of indices are generalized fuzzy numbers. The proposed mechanism is based on the fuzzy-number similarity measure. Firstly determine the linguistic evaluating values and the linguistic weights of each evaluating criterion with respect to the alternatives. Then measure the degree of similarity between two arbitrary weighted evaluating values on the same criterion. Finally constructing the hierarchical cluster tree and generated different clusters. A numerical example is demonstrated the new mechanism.
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Wei, Shih-Hua, and 魏世驊. "New Methods for Fuzzy Risk Analysis Based on Similarity Measures between Fuzzy Numbers." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/am43ea.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
95
In recent years, the task of measuring the degree of similarity between fuzzy numbers plays an important role in fuzzy decision making, information fusion and fuzzy risk analysis. In this thesis, we present two similarity measures for generalized fuzzy numbers and interval-valued fuzzy numbers. It combines the concepts of geometric distance, the perimeter, height and center of gravity point of generalized fuzzy numbers and interval-valued fuzzy number, respectively. Moreover, we also presented an interval-valued fuzzy number adjusting algorithm. Based on proposed similarity measures, we propose two new methods for handling fuzzy risk analysis problems. The proposed fuzzy risk analysis methods can overcome the drawback of existing methods. They can deal with fuzzy risk analysis in a more intelligent and flexible manner.
33
Xiong, Xuejian, and Kian Lee Tan. "Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering." 2003. http://hdl.handle.net/1721.1/3872.
Full textAbstract:
In this paper, a similarity-driven cluster merging method is proposed for unsupervised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-driven cluster merging criterion. The similarity between clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive threshold is used for merging. In addition, a modified generalized objective function is used for prototype-based fuzzy clustering. The function includes the p-norm distance measure as well as principal components of the clusters. The number of the principal components is determined automatically from the data being clustered. The performance of this unsupervised fuzzy clustering algorithm is evaluated by several experiments of an artificial data set and a gene expression data set.Singapore-MIT Alliance (SMA)
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Tsai, Chia-Chia, and 蔡嘉嘉. "Fuzzy Multi-Categorization and Similarity Analysis of Electronic Document." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/86424391460484802247.
Full textAbstract:
碩士國立高雄第一科技大學
資訊管理所
91
Thanks to the proliferation of Internet, documents are rapidly shared over the cyberspace in the past few years. However, it also makes knowledge workers suffer from the information-overloading problem. For a set of documents without suitable categorization, the searching process will be prone to time-consuming. To overcome the problem, the issues about effective information retrieval have been studied extensively. The objective mainly focuses on how to match documents that are conforming to users’ requirements efficiently. Nevertheless, in the real world applications, people tend to use some ‘fuzzy’ terms to express their thinking and there are always ambiguity and uncertainty existing in the procedure. Beside, a document may involve various concerns, which makes it should be better categorized into multiple categories. In this paper, we propose a multiple categorization approach based on fuzzy set theory to classify documents into multiple categories. Furthermore, by using the concepts of fuzzy correlation coefficient and the similarity tree graph, we can proceed to document similarity analysis and accomplish the work of hierarchical categorization. Finally we conduct experiments over a set of conference papers to verify the precision rate and recall rate of our approach compared with manual categorizations. Generally speaking, due to the complex semantics involved in various documents, manual categorization can realize the most suitable topic a document belongs if the content is carefully scanned or digested. However, the process is usually time-consuming, which makes most of the categorization tasks be simply based on the document titles or abstracts. Our approach can help to alleviate such problem and provide a multi-categorization solution for most of the text categorization applications. In the future, the result can be further utilized as a base for constructing a document warehouse by applying text-mining methods.
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Kuo, Chun-Yin, and 郭春吟. "Data Mining of Fuzzy Rules Based on Similarity relations." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/87019230665876961691.
Full textAbstract:
碩士義守大學
資訊工程學系
90
The goal of this research is to define new types of association rules, sequential patterns, and to develop related data mining algorithms for these new rules, due to the characteristics of similarities between data. Knowledge discovery in databases means a process of nontrivial extraction of implicit, previously unknown and potential useful information from databases [27]. Most data mining algorithms consider two types of databases, transactional and relational, and deal with data with binary, numeric, interval, and fuzzy values, etc. However, without considering the application granularity and domain knowledge, extensive computation will be required to produce vast amount of rules and/or patterns that may be impractical for direct application. Concept hierarchy (or taxonomy) is an example of considering domain knowledge and extracts rules with different levels of granularities. Similarity relation is a kind of domain knowledge that can represent the similarities among database item. In this work, we propose four new types of association rules and sequential patterns (Similar Association Rule, Similar Sequential Pattern, Fuzzy Similar Association Rule, Fuzzy Similar Sequential Pattern) and their corresponding mining algorithms. In addition, we carry out analysis and experiments on the performance and characteristics of similar association rule mining algorithm. We found that similar association rule and association rule have similar characteristics but with more flexibilities and potential application values.
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CHEN, YI-CHING, and 陳俋晴. "A Similarity Measure between Intuitionistic Fuzzy Numbers and Applications." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/dm24nn.
Full textAbstract:
碩士國立高雄科技大學
工業工程與管理系
107
This paper analyzes the counterintuitive behaviors of adopted twelve distance based similarity measures between two intuitionistic fuzzy sets. Among these distance-based similarity measures, the largest number of components of the distance in the similarity measure is four for the third Liang and Shi’s (2003) similarity measure. We propose six general counterintuitive test problems to analyze their counterintuitive behaviors. The results indicate that all the distance based similarity measures have some counterintuitive test problems. Furthermore, for the third Liang and Shi’s (2003) similarity measure, four types of counterintuitive examples exist. Therefore, the counterintuitive behaviors are inevitable for the distance based similarity measures between intuitionistic fuzzy sets.
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KUMAR, AJAY. "EDGE DETECTION USING BACTERIA FORAGING & FUZZY SIMILARITY MEASURE." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14024.
Full textAbstract:
Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image.In the proposed method, the bacteria foraging is used along with contemporary fuzzy logic which implements a relative pixel similarity value algorithm.The similarity between two pixels is calculated using the weighted participation of each fuzzy rule. Low similarity between the pixels represents the probability of a pixel to be an edge pixel. The bacteria moves over the low similarity region, thus maximizing the edge content while minimizing the presence of non-edge content in the movement path. Directional Pixel Similarity is used to locate the similar pixel to the edge pixel and thus the movement of bacteria is decided.Bacteria with sufficient nutrients are reproduced, i.e., at the intersection of more than one edge a bacterium will split into the number of edges. If a bacterium found itself low on nutrients than it will be eliminated from its current location and dispersed to some other location. The path traced by the bacteria is the edge map. Thus with proposed method, edge pixels in a color image are detected simultaneously without any complex calculations such as gradient, Laplace and statistical calculations.
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Kao, Hsiao-Wei, and 高曉薇. "New Fuzzy-Number Similarity Measures and Prioritized Information Fusion Mechanisms for Fuzzy Recommendation Problems." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/20907517971403212545.
Full textAbstract:
碩士國立聯合大學
管理碩士學位學程
98
In this thesis, we propose a new method for measuring the degree of similarity between generalized fuzzy numbers based on standard deviation. Some properties of the proposed similarity measure are demonstrated, and 44 sets of generalized fuzzy numbers are applied to compare the proposed method with existing similarity measures. Furthermore, the proposed similarity measure is used to solve fuzzy recommendation problems. A decision maker’s evaluations of parameters or variables involve with real-world problems that can be represented by interval-valued fuzzy numbers. Therefore, we also present a new similarity measured method that based on the standard deviation operator to solve before similarity measurement between interval-valued fuzzy numbers. In addition, some properties of the proposed similarity measure have been demonstrated, and 17 sets of interval-valued fuzzy numbers are adopted to compare the proposed method with existing similarity measures. Furthermore, the proposed similarity measure is used to deal with fuzzy recommendation problems. Chen and Chen [23] and Hong et. al [40] presented new prioritized information fusion algorithm for handling fuzzy information retrieval problems. However, according to our research, these algorithms still have the following drawbacks. Thence, we present a new prioritized information fusion algorithm based on based on GMA operator and fuzzy-number similarity measure to deal with prioritized multi-criteria fuzzy decision-making problems and prioritized information filtering problems based on generalized fuzzy numbers. However, some researchers have pointed out that using interval-valued fuzzy numbers for representing linguistic terms improves flexibility. Thence, we also present a new prioritized information fusion algorithm for handling information filtering problems based on interval-valued fuzzy numbers. Furthermore, we use the proposed fusion algorithm for handling information filtering problems. The proposed prioritized information fusion algorithm can deal with information filtering problems in a more flexible manner.
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Chang, Hsiu Yu, and 張修毓. "A study on consensus-reaching process using intuitionistic fuzzy similarity." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/58304858306469129870.
Full textAbstract:
碩士長庚大學
資訊管理學研究所
97
There are many decisions to make in our daily lives. When facing some problems, group decision is often more reliable than individual decision. More over, the consensus-reaching process is considered the most important process in all group decision methods. Implementing intuitionistic fuzzy similarities into such process can lower the degree of hesitation during group decision. In this research, we proceeded a simulation experiment in the consensus-reaching process with intuitionistic fuzzy similarity. The simulation data included different sizes of decision matrices in different number of alternatives, attributes, and experts. Then we compare each similarity measure arranged by previous researchers according to the ranking results. Finally, we discussed the result of alternative ranking influenced by all kinds of intuitionistic fuzzy similarities. According to our experiment analysis, when few experts participate, most of the intuitionistic fuzzy similarities resulted in similar ranking during the consensus-reaching process. Nevertheless, the rankings diversed as the number of experts increased from 3 to 12, and the Spearman correlation coefficient finally resulted in -0.5. As a result, when carrying out consensus-reaching process with intuitionistic fuzzy similarity in group decision, we should be cautious with the similarity measures according to the number of experts participated in decisions.
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Li, Hou Hsun, and 李後勳. "A Study of Intuitionistic Fuzzy Similarity Measures on Group Decision Making." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/55025887950799017965.
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碩士長庚大學
企業管理研究所
97
In 1965, fuzzy sets were introduced by Zadeh to deal with the imprecise and variable data in real life. In several decades later, intuitionistic fuzzy set theory were developed by Atanassov to express the human feeling with numerical numbers, and were proposed in group decision making. One practical application is similarity measures. Naturally, at the beginning of every group decision making problem, experts’ opinions may differ substantially. Therefore, it is necessary to develop a consensus process in an attempt to obtain the maximum degree of consensus or agreement between the set of experts on the solution set of alternatives. Consensus model process is processed to integrate a single opinion into group opinions, which could be ranked by numerical numbers. The aim of this paper is to present a consensus model for solving group decision making problems in intuitionistic fuzzy set environment. We conduct the proposed method on different similarity measures to discuss the effect to the ranking result. The similarity measures are separated into four groups from each similarity measure expression and its own measuring focus. In addition, a comprehensive experimental analysis to observe the intuitionistic fuzzy consensus results yielded by different similarity measures is presented. Several comparison indices are examined, including the average Spearman correlation coefficients, the consistency rate, the inversion rate, and the contradiction rate. According to the results, the four indices are affected as the number of decision alternatives in a problem increases. On the other hand, the number of attributes, and the number of experts have only a minor practical influence in view of the four indices. We suggest forward researchers that it might be a better choice to apply a simple and measure.
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張建瑋. "Application of Fuzzy Statistics in Time Series Analysis and Similarity Recognition." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/34854174790352568313.
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碩士國立政治大學
應用數學研究所
85
All important problem in pattern recognition of a time series is similarity recognition. This paper presents the methods of similarity calculation for two time series. The methods considered include equally divided range method, K-rneans method and rank transformed method. The success of our similarity recognition relies a large extent on the fuzzy statistical concept. Simulation results demonstrate that, overall, the equally divided range method performed best in the similarity recognition. While other methods provide superior efficiency in calculating similarity for certain special time series. Finally two empirical examples, similarity calculating about GDP vs. Consumption and GDP vs. Invest, are illustrated.
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Liao, Z.-Han, and 廖姿涵. "On similarity, inclusion and entropy measures between interval-valued fuzzy sets." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/88412490306171034612.
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碩士中原大學
應用數學研究所
103
Interval-valued fuzzy sets (IVFSs) are an extension of fuzzy sets. Since IVFSs present fuzzy sets with interval-valued memberships, they could have more widely for uncertainty modeling than fuzzy sets and are also easier to handle in practice than type-2 fuzzy sets. It is known that similarity, inclusion and entropy are the three important measures for fuzzy concepts. In this paper, we first propose new inclusion measures for IVFSs. We then derive similarity measures between IVFSs based on these inclusion measures. Furthermore, we take a weighted average of these two similarity measures between IVFSs and then construct new entropy measures for IVFSs. Some properties of these new similarity, inclusion and entropy measures between IVFSs are made. We also make numerical comparisons of the proposed measures with some existing measures. These comparison results show the superiority of the proposedmeasures.
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Chang-Chien, Shou-Jen, and 張簡守仁. "A similarity measure between LR-type fuzzy numbers and its applications." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/73523095356599311008.
Full textAbstract:
碩士中原大學
數學研究所
90
This paper presents a new similarity measure for LR-type fuzzy numbers. The proposed similarity measure is based on a defined metric between LR-type fuzzy numbers. We then analyze its properties and make numerical comparisons to several similarities. The results show that the proposed similarity measure has better defection for the difference of the degree of similarities between LR-type fuzzy numbers. Finally, we apply it to generate compound attributes for handling null queries to database systems. These applications can be widely need in fuzzy queries to database.
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CHOU, CHIA-YU, and 周家宇. "Incremental Enterprise Email Classification Based on Cosine and Fuzzy Similarity Approaches." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/36388601637484688236.
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碩士國立中央大學
資訊工程研究所
100
Nowadays, the usage amounts of email have increased because Internet becomes more common. Many enterprises regard email as an essential way for business in contacting with customers or employees. Therefore, the management of email system becomes even more important for an enterprise. However, it is unavoidable that a lot of employees send private emails by enterprise email system. It has brought negative effect to email system because the bandwidths are used by personal purpose. What worse, it may delay or affect in sending significant business emails. It may decrease the interests of an enterprise. Moreover, public becomes to take care about privacy. How to classify enterprise emails as either business or personal emails to improve the business interests without monitoring the contents of email. This is the goal of the paper. To achieve this purpose, only the header of email will be used. The contents in this paper will not. Although it may lower the accuracy of classification. It will protect employee’s private rights. Using the cosine similarity and fuzzy similarity approaches to classify enterprise emails by extracted email header. More important, the incremental system which this paper purposed could effectively avoid handling the huge amount of cumulate emails. And it also considers the change of internal staffs or customers of an enterprise with passing of time.
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Chang, Chia-Hao, and 張家豪. "New Similarity Measure Between Intuitionistic Fuzzy Sets and New Fuzzy Multiattribute Decision Making Method Based on Intuitionistic Fuzzy Geometric Averaging Opeators." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/31793339400809159151.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
102
Fuzzy multiattribute decision making based on intuitionistic fuzzy sets is an important research topic. In recent years, some methods have been presented for dealing with fuzzy multiattribute decision making problems. In this thesis, we propose a new similarity measure between intuitionistic fuzzy sets and propose a new fuzzy multiattribute decision making method based on the proposed intuitionistic fuzzy geometric averaging operators. First, we propose a new similarity measure between intuitionistic fuzzy sets. Then, we propose the intuitionistic fuzzy weighted geometric averaging (IFWGA) operator, the intuitionistic fuzzy ordered weighted geometric averaging (IFOWGA) operator and the intuitionistic fuzzy hybrid geometric averaging (IFHGA) operator based on the proposed multiplication operator between intuitionistic fuzzy values and the proposed power operator of an intuitionistic fuzzy value. Then, we propose a new fuzzy multiattribute decision making method based on the proposed intuitionistic fuzzy geometric averaging operators. Finally, we use some examples to illustrate the proposed fuzzy multiattribute decision making method can overcome the drawbacks of the existing methods. The proposed method provides us with a useful way to deal with fuzzy multiattribute decision making problems based on intuitionistic fuzzy sets.
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Chen, Chia-Ling, and 陳佳伶. "New Fuzzy Interpolative Reasoning Methods Based on Ranking Values of Polygonal Fuzzy Sets, Automatically Generated Weights of Fuzzy Rules and Similarity Measures Between Polygonal Fuzzy Sets." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47496074659379726144.
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碩士國立臺灣科技大學
資訊工程系
103
Fuzzy interpolative reasoning is a very important research topic for sparse fuzzy rule-based systems. In this thesis, we propose two new fuzzy interpolative reasoning methods for sparse fuzzy rule-based systems based on polygonal fuzzy sets and the ranking values of polygonal fuzzy sets. In the first method of our thesis, we propose a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on ranking values of polygonal fuzzy sets and automatically generated weights of fuzzy rules. The experimental results show that the proposed method can overcome the drawbacks of the existing fuzzy interpolative reasoning methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems. In the second method of our thesis, we propose a new adaptive fuzzy interpolation method based on ranking values of polygonal fuzzy sets and similarity measures between polygonal fuzzy sets. The proposed adaptive fuzzy interpolation method performs fuzzy interpolative reasoning using multiple fuzzy rules with multiple antecedent variables and solves the contradictions after the fuzzy interpolative reasoning processes based on similarity measures between polygonal fuzzy sets. The experimental results show that the proposed adaptive fuzzy interpolation method outperforms the existing methods for fuzzy interpolative reasoning in sparse fuzzy rule-based systems.
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Meghdadi, Amir Hossein. "Fuzzy Tolerance Neighborhood Approach to Image Similarity in Content-based Image Retrieval." 2012. http://hdl.handle.net/1993/8094.
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The main contribution of this thesis, is to define similarity measures between two images with the main focus on content-based image retrieval (CBIR). Each image is considered as a set of visual elements that can be described with a set of visual descriptions (features). The similarity between images is then defined as the nearness between sets of elements based on a tolerance and a fuzzy tolerance relation.
A tolerance relation is used to describe the approximate nature of the visual perception. A fuzzy tolerance relation is adopted to eliminate the need for a sharp threshold and hence model the gradual changes in perception of similarities. Three real valued similarity measures as well as a fuzzy valued similarity measure are proposed. All of the methods are then used in two CBIR experiments and the results are compared with classical measures of distance (namely, Kantorovich, Hausdorff and Mahalanobis). The results are compared with other published research papers. An important advantage of the proposed methods is shown to be their effectiveness in an unsupervised setting with no prior information. Eighteen different features (based on color, texture and edge) are used in all the experiments. A feature selection algorithm is also used to train the system in choosing a suboptimal set of visual features.
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Li, Yi-Wen, and 黎怡妏. "The Intuitionistic Fuzzy TOPSIS Method and Discussions on Distance and Similarity Measures." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/64491054869290692252.
Full textAbstract:
碩士長庚大學
企業管理研究所
95
In our daily lives, when make a decision, we would consider lots of criteria. Sometimes, some of them would have conflict in alternatives. To choose the best alternative, we would give criteria weights and depend on the importance of criteria. It has its calculation in different multiple attribute decision making methods. There are many MADM methods in decision making and TOPSIS is the most widely used in MADM. It can apply in many areas, such as plant layout, group decision making, enterprise networks and dynamic operator allocation problem. In past researches, most of them emphasize on the way of TOPSIS, such as data collection, fuzzy number or interval data. We use distance measure and similarity measure to define the separation measure. There are few researches about the separation measure. Furthermore, we make data of TOPSIS intuitionistic fuzzy sets. The reason is intuitionistic fuzzy sets have the degree of hesitation than fuzzy sets. This is because hesitation exists on our life and in the decision making. Therefore, we combine intuitionistic fuzzy sets and replace the element of decision matrix of TOPSIS to discuss the result of alternative ranking in all kinds of distance measures in our research. We select difference distance measures and use intuitionistic fuzzy sets and similarity measures to define separation measure. Through our experiment analysis, we find out that it has enormous difference result by difference distance measures. Overall, their consistency rates are low and they are between 0.0014 to 0.0030. Furthermore, when we use TOPSIS we would choose the best alternative. However, the best alternative has a low contradiction rate. Moreover, some distance equations is the worse ranking, but in others ranking is better ranking. Therefore, we find out that difference distance measures have a lot impact on final alternative ranking result of TOPSIS. According to the spearman correlation coefficients, we know their correlation coefficients are between 0.1680 to 0.8910. Especially when the numbers of alternatives and attributes are small and their average spearman correlation coefficients are large. However, their standard deviations are also large and unstable. Overall, the number of alternatives and attributes increase and the average spearman correlation coefficients decrease. Therefore, distance measures play an importance role to the final ranking result and it has no relevant research in the past.
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Yu, Wen Chieh, and 游文杰. "Handwritten Character Recognition Using Fuzzy OR-AND-Difference Operator and Similarity Degree." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/96516895735472583231.
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Chen, Jim-Ho, and 陳進和. "New Methods for Fuzzy Risk Analysis Based on Ranking Generalized Fuzzy Numbers and Similarity Measures between Interval-Valued." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/3h94qt.
Full textAbstract:
碩士國立臺灣科技大學
資訊工程系
95
In this thesis, we present two methods for fuzzy risk analysis based on ranking generalized fuzzy numbers and similarity measures between interval-valued fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers for handling fuzzy risk analysis problems. The proposed method considers defuzzified values, the weight and the spreads of generalized fuzzy numbers. Moreover, we also apply the proposed method for ranking generalized fuzzy numbers to present a new method for dealing with fuzzy risk analysis problems. Then, we present a new similarity measure for interval-valued fuzzy numbers. The proposed similarity measure considers five factors, i.e., the degree of similarity on X-axis between the upper fuzzy numbers of the interval-valued fuzzy numbers, the degree of similarity about the weight of the upper fuzzy numbers of the interval-valued fuzzy numbers, the spread between the upper fuzzy numbers of the interval-valued fuzzy numbers, the degree of similarity on the X-axis between the interval-valued fuzzy numbers, and the degree of similarity on the Y-axis between the interval-valued fuzzy numbers. Moreover, we also present new interval-valued fuzzy numbers arithmetic operators and apply the proposed similarity measure to present a new method for dealing with fuzzy risk analysis problems based on interval-valued fuzzy numbers. The proposed fuzzy risk analysis methods provide us a useful way for handling fuzzy risk analysis problems.