Academic literature on the topic 'Fuzzy formal concept analysis'
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Journal articles on the topic "Fuzzy formal concept analysis"
Supriyati, Endang. "FUZZY FORMAL CONCEPT ANALYSIS UNTUK KEMIRIPAN DOKUMEN." Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 1, no. 1 (June 29, 2013): 21. http://dx.doi.org/10.24176/simet.v1i1.111.
Full textFORMICA, ANNA. "CONCEPT SIMILARITY IN FUZZY FORMAL CONCEPT ANALYSIS FOR SEMANTIC WEB." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (April 2010): 153–67. http://dx.doi.org/10.1142/s0218488510006465.
Full textHaddache, Mohamed, Allel Hadjali, and Hamid Azzoune. "Skyline refinement exploiting fuzzy formal concept analysis." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (April 29, 2021): 333–62. http://dx.doi.org/10.1108/ijicc-11-2020-0181.
Full textKonecny, Jan, and Ondrej Krídlo. "On biconcepts in formal fuzzy concept analysis." Information Sciences 375 (January 2017): 16–29. http://dx.doi.org/10.1016/j.ins.2016.09.042.
Full textKonecny, Jan, and Michal Krupka. "Block relations in formal fuzzy concept analysis." International Journal of Approximate Reasoning 73 (June 2016): 27–55. http://dx.doi.org/10.1016/j.ijar.2016.02.004.
Full textShao, Ming-Wen, Min Liu, and Wen-Xiu Zhang. "Set approximations in fuzzy formal concept analysis." Fuzzy Sets and Systems 158, no. 23 (December 2007): 2627–40. http://dx.doi.org/10.1016/j.fss.2007.05.002.
Full textAlwersh, Mohammed, and Kovács László. "Fuzzy formal concept analysis: approaches, applications and issues." Computer Science and Information Technologies 3, no. 2 (July 1, 2022): 126–36. http://dx.doi.org/10.11591/csit.v3i2.p126-136.
Full textPoelmans, Jonas, Dmitry I. Ignatov, Sergei O. Kuznetsov, and Guido Dedene. "Fuzzy and rough formal concept analysis: a survey." International Journal of General Systems 43, no. 2 (January 6, 2014): 105–34. http://dx.doi.org/10.1080/03081079.2013.862377.
Full textLiu, Yan, Sheng Quan Liu, and Peng Li. "Tourism Domain Ontology Construction Method Based on Fuzzy Formal Concept Analysis." Applied Mechanics and Materials 347-350 (August 2013): 2809–13. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2809.
Full textWang, Ting Zhong, and Hong Sheng Xu. "Constructing Domain Ontology Based on Fuzzy Set and Concept Lattice." Applied Mechanics and Materials 63-64 (June 2011): 715–18. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.715.
Full textDissertations / Theses on the topic "Fuzzy formal concept analysis"
De, Maio Carmen. "Fuzzy concept analysis for semantic knowledge extraction." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/1307.
Full textAvailability of controlled vocabularies, ontologies, and so on is enabling feature to provide some added values in terms of knowledge management. Nevertheless, the design, maintenance and construction of domain ontologies are a human intensive and time consuming task. The Knowledge Extraction consists of automatic techniques aimed to identify and to define relevant concepts and relations of the domain of interest by analyzing structured (relational databases, XML) and unstructured (text, documents, images) sources. Specifically, methodology for knowledge extraction defined in this research work is aimed at enabling automatic ontology/taxonomy construction from existing resources in order to obtain useful information. For instance, the experimental results take into account data produced with Web 2.0 tools (e.g., RSS-Feed, Enterprise Wiki, Corporate Blog, etc.), text documents, and so on. Final results of Knowledge Extraction methodology are taxonomies or ontologies represented in a machine oriented manner by means of semantic web technologies, such as: RDFS, OWL and SKOS. The resulting knowledge models have been applied to different goals. On the one hand, the methodology has been applied in order to extract ontologies and taxonomies and to semantically annotate text. On the other hand, the resulting ontologies and taxonomies are exploited in order to enhance information retrieval performance and to categorize incoming data and to provide an easy way to find interesting resources (such as faceted browsing). Specifically, following objectives have been addressed in this research work: Ontology/Taxonomy Extraction: that concerns to automatic extraction of hierarchical conceptualizations (i.e., taxonomies) and relations expressed by means typical description logic constructs (i.e., ontologies). Information Retrieval: definition of a technique to perform concept-based the retrieval of information according to the user queries. Faceted Browsing: in order to automatically provide faceted browsing capabilities according to the categorization of the extracted contents. Semantic Annotation: definition of a text analysis process, aimed to automatically annotate subjects and predicates identified. The experimental results have been obtained in some application domains: e-learning, enterprise human resource management, clinical decision support system. Future challenges go in the following directions: investigate approaches to support ontology alignment and merging applied to knowledge management.
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Konecny, Jan. "Isotone fuzzy Galois connections and their applications in formal concept analysis." Diss., Online access via UMI:, 2009.
Find full textIncludes bibliographical references.
Glodeanu, Cynthia Vera. "Conceptual Factors and Fuzzy Data." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-103775.
Full textKomplexitätsreduktion ist eines der wichtigsten Verfahren in der Datenanalyse. Mit ständig wachsenden Datensätzen gilt dies heute mehr denn je. In vielen Gebieten stößt man zudem auf vage und ungewisse Daten. Wann immer man ein Instrument zur Datenanalyse hat, stellen sich daher die folgenden zwei Fragen auf eine natürliche Weise: Wie kann man im Rahmen der Analyse die Variablenanzahl verkleinern, und wie kann man Fuzzy-Daten bearbeiten? In dieser Arbeit versuchen wir die eben genannten Fragen für die Formale Begriffsanalyse zu beantworten. Genauer gesagt, erarbeiten wir verschiedene Methoden zur Komplexitätsreduktion qualitativer Daten und entwickeln diverse Verfahren für die Bearbeitung von Fuzzy-Datensätzen. Basierend auf diesen beiden Themen gliedert sich die Arbeit in zwei Teile. Im ersten Teil liegt der Schwerpunkt auf der Komplexitätsreduktion, während sich der zweite Teil der Verarbeitung von Fuzzy-Daten widmet. Die verschiedenen Kapitel sind dabei durch die beiden Themen verbunden. So werden insbesondere auch Methoden für die Komplexitätsreduktion von Fuzzy-Datensätzen entwickelt
Ayouni, Sarra. "Etude et Extraction de règles graduelles floues : définition d'algorithmes efficaces." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20015/document.
Full textKnowledge discovery in databases is a process aiming at extracting a reduced set of valuable knowledge from a huge amount of data. Data mining, one step of this process, includes a number of tasks, such as clustering, classification, of association rules mining, etc.The problem of mining association rules requires the step of frequent patterns extraction. We distinguish several categories of frequent patterns: classical patterns, fuzzy patterns, gradual patterns, sequential patterns, etc. All these patterns differ on the type of the data from which the extraction is done and the type of the relationship that represent.In this thesis, we particularly contribute with the proposal of fuzzy and gradual patterns extraction method.Indeed, we define new systems of closure of the Galois connection for, respectively, fuzzy and gradual patterns. Thus, we propose algorithms for extracting a reduced set of fuzzy and gradual patterns.We also propose two approaches for automatically defining fuzzy modalities that allow obtaining relevant fuzzy gradual patterns.Based on fuzzy closed and gradual closed patterns, we define generic bases of fuzzy and gradual association rules. We thus propose a complet and valid inference system to derive all redundant fuzzy and gradual association rules
Novi, Daniele. "Knowledge management and Discovery for advanced Enterprise Knowledge Engineering." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1466.
Full textThe research work addresses mainly issues related to the adoption of models, methodologies and knowledge management tools that implement a pervasive use of the latest technologies in the area of Semantic Web for the improvement of business processes and Enterprise 2.0 applications. The first phase of the research has focused on the study and analysis of the state of the art and the problems of Knowledge Discovery database, paying more attention to the data mining systems. The most innovative approaches which were investigated for the "Enterprise Knowledge Engineering" are listed below. In detail, the problems analyzed are those relating to architectural aspects and the integration of Legacy Systems (or not). The contribution of research that is intended to give, consists in the identification and definition of a uniform and general model, a "Knowledge Enterprise Model", the original model with respect to the canonical approaches of enterprise architecture (for example with respect to the Object Management - OMG - standard). The introduction of the tools and principles of Enterprise 2.0 in the company have been investigated and, simultaneously, Semantic Enterprise based appropriate solutions have been defined to the problem of fragmentation of information and improvement of the process of knowledge discovery and functional knowledge sharing. All studies and analysis are finalized and validated by defining a methodology and related software tools to support, for the improvement of processes related to the life cycles of best practices across the enterprise. Collaborative tools, knowledge modeling, algorithms, knowledge discovery and extraction are applied synergistically to support these processes. [edited by author]
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Dao, Ngoc Bich. "Réduction de dimension de sac de mots visuels grâce à l’analyse formelle de concepts." Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS010/document.
Full textIn several scientific fields such as statistics, computer vision and machine learning, redundant and/or irrelevant information reduction in the data description (dimension reduction) is an important step. This process contains two different categories : feature extraction and feature selection, of which feature selection in unsupervised learning is hitherto an open question. In this manuscript, we discussed about feature selection on image datasets using the Formal Concept Analysis (FCA), with focus on lattice structure and lattice theory. The images in a dataset were described as a set of visual words by the bag of visual words model. Two algorithms were proposed in this thesis to select relevant features and they can be used in both unsupervised learning and supervised learning. The first algorithm was the RedAttSansPerte, which based on lattice structure and lattice theory, to ensure its ability to remove redundant features using the precedence graph. The formal definition of precedence graph was given in this thesis. We also demonstrated their properties and the relationship between this graph and the AC-poset. Results from experiments indicated that the RedAttsSansPerte algorithm reduced the size of feature set while maintaining their performance against the evaluation by classification. Secondly, the RedAttsFloue algorithm, an extension of the RedAttsSansPerte algorithm, was also proposed. This extension used the fuzzy precedence graph. The formal definition and the properties of this graph were demonstrated in this manuscript. The RedAttsFloue algorithm removed redundant and irrelevant features while retaining relevant information according to the flexibility threshold of the fuzzy precedence graph. The quality of relevant information was evaluated by the classification. The RedAttsFloue algorithm is suggested to be more robust than the RedAttsSansPerte algorithm in terms of reduction
Diner, Casri. "Visualizing Data With Formal Concept Analysis." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1046325/index.pdf.
Full texts hard-disk capacities which is used for storing datas and the amount of data you can reach through internet is increasing day by day, there should be a need to turn this information into knowledge. This is one of the reasons for studying formal concept analysis. We wanted to point out how this application is related with algebra and logic. The beginning of the first chapter emphasis the relation between closure systems, galois connections, lattice theory as a mathematical structure and concept analysis. Then it describes the basic step in the formalization: An elementary form of the representation of data is defined mathematically. Second chapter explains the logic of formal concept analysis. It also shows how implications, which can be regard as special formulas on a set,between attributes can be shown by fewer implications, so called generating set for implications. These mathematical tools are then used in the last chapter, in order to describe complex '
concept'
lattices by means of decomposition methods in examples.
Krajča, Petr. "Advanced algorithms for formal concept analysis." Diss., Online access via UMI:, 2009.
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Petersen, Wiebke, and Petja Heinrich. "Qualitative Citation Analysis Based on Formal Concept Analysis." Universitätsbibliothek Chemnitz, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200801464.
Full textSertkaya, Baris. "Formal Concept Analysis Methods for Description Logics." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1215598189927-85390.
Full textBooks on the topic "Fuzzy formal concept analysis"
Braud, Agnès, Aleksey Buzmakov, Tom Hanika, and Florence Le Ber, eds. Formal Concept Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77867-5.
Full textCristea, Diana, Florence Le Ber, and Baris Sertkaya, eds. Formal Concept Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21462-3.
Full textKwuida, Léonard, and Barış Sertkaya, eds. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11928-6.
Full textFerré, Sébastien, and Sebastian Rudolph, eds. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01815-2.
Full textCellier, Peggy, Felix Distel, and Bernhard Ganter, eds. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38317-5.
Full textDomenach, Florent, Dmitry I. Ignatov, and Jonas Poelmans, eds. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29892-9.
Full textValtchev, Petko, and Robert Jäschke, eds. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20514-9.
Full textGlodeanu, Cynthia Vera, Mehdi Kaytoue, and Christian Sacarea, eds. Formal Concept Analysis. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07248-7.
Full textGanter, Bernhard, and Rudolf Wille. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59830-2.
Full textMedina, Raoul, and Sergei Obiedkov, eds. Formal Concept Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78137-0.
Full textBook chapters on the topic "Fuzzy formal concept analysis"
Macko, Juraj. "User-Friendly Fuzzy FCA." In Formal Concept Analysis, 156–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38317-5_10.
Full textBělohlávek, Radim, Vladimír Sklenář, and Jiří Zacpal. "Crisply Generated Fuzzy Concepts." In Formal Concept Analysis, 269–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32262-7_19.
Full textBrito, Abner, Laécio Barros, Estevão Laureano, Fábio Bertato, and Marcelo Coniglio. "Fuzzy Formal Concept Analysis." In Communications in Computer and Information Science, 192–205. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95312-0_17.
Full textBělohlávek, Radim, and Vilém Vychodil. "Attribute Implications in a Fuzzy Setting." In Formal Concept Analysis, 45–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11671404_3.
Full textGarcía-Pardo, F., I. P. Cabrera, P. Cordero, and M. Ojeda-Aciego. "On Closure Systems and Adjunctions Between Fuzzy Preordered Sets." In Formal Concept Analysis, 114–27. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19545-2_7.
Full textAntoni, Lubomir, Stanislav Krajči, and Ondrej Krídlo. "Randomized Fuzzy Formal Contexts and Relevance of One-Sided Concepts." In Formal Concept Analysis, 183–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19545-2_12.
Full textKonecny, Jan. "Bonds Between $$L$$ -Fuzzy Contexts Over Different Structures of Truth-Degrees." In Formal Concept Analysis, 81–96. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19545-2_5.
Full textLiu, Xiaodong, and Witold Pedrycz. "AFS Formal Concept and AFS Fuzzy Formal Concept Analysis." In Axiomatic Fuzzy Set Theory and Its Applications, 303–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00402-5_8.
Full textCordero, Pablo, Manuel Enciso, and Angel Mora. "Directness in Fuzzy Formal Concept Analysis." In Communications in Computer and Information Science, 585–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91473-2_50.
Full textDjouadi, Yassine, and Henri Prade. "Interval-Valued Fuzzy Formal Concept Analysis." In Lecture Notes in Computer Science, 592–601. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04125-9_62.
Full textConference papers on the topic "Fuzzy formal concept analysis"
Golinska-Pilarek, Joanna, and Ewa Orlowska. "Relational Reasoning in Formal Concept Analysis." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295512.
Full textZhou, Lei. "Formal concept analysis in intuitionistic fuzzy formal context." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569658.
Full textShen, Lili, and Dexue Zhang. "Formal concept analysis on fuzzy sets." In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013. http://dx.doi.org/10.1109/ifsa-nafips.2013.6608402.
Full textKridlo, Ondrej, and Manuel Ojeda-Aciego. "Extending formal concept analysis using intuitionistic l-fuzzy sets." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015570.
Full textCross, Valerie V., and Wenting Yi. "Formal concept analysis for ontologies and their annotation files." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630646.
Full textHashemi, R. R., S. De Agostino, B. Westgeest, and J. R. Talburt. "Data granulation and formal concept analysis." In IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. IEEE, 2004. http://dx.doi.org/10.1109/nafips.2004.1336253.
Full textZhou, Wen, Zongtian Liu, and Yan Zhao. "Concept Hierarchies Generation for Classification using Fuzzy Formal Concept Analysis." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.229.
Full textBelohlavek, Radim, and Jan Konecny. "Scaling, Granulation, and Fuzzy Attributes in Formal Concept Analysis." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295488.
Full textYao, Y. Y., and Yaohua Chen. "Rough set approximations in formal concept analysis." In IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. IEEE, 2004. http://dx.doi.org/10.1109/nafips.2004.1336252.
Full textLiu, Jun, and Xiaoqiu Yao. "Formal concept analysis of incomplete information system." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569661.
Full textReports on the topic "Fuzzy formal concept analysis"
Baader, Franz, Bernhard Ganter, Ulrike Sattler, and Barış Sertkaya. Completing Description Logic Knowledge Bases using Formal Concept Analysis. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.155.
Full textBaader, Franz, and Felix Distel. A finite basis for the set of EL-implications holding in a finite model. Technische Universität Dresden, 2007. http://dx.doi.org/10.25368/2022.160.
Full textBaader, Franz, and Felix Distel. Exploring finite models in the Description Logic ELgfp. Technische Universität Dresden, 2008. http://dx.doi.org/10.25368/2022.168.
Full textBorchmann, Daniel. A General Form of Attribute Exploration. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.192.
Full textBorchmann, Daniel. Exploration by Confidence. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.194.
Full textСоловйов, Володимир Миколайович, and V. Saptsin. Heisenberg uncertainty principle and economic analogues of basic physical quantities. Transport and Telecommunication Institute, 2011. http://dx.doi.org/10.31812/0564/1188.
Full textSoloviev, V., V. Solovieva, and V. Saptsin. Heisenberg uncertainity principle and economic analogues of basic physical quantities. Брама-Україна, 2014. http://dx.doi.org/10.31812/0564/1306.
Full textSoloviev, V., and V. Solovieva. Quantum econophysics of cryptocurrencies crises. [б. в.], 2018. http://dx.doi.org/10.31812/0564/2464.
Full textKelly, Luke. Definitions, Characteristics and Monitoring of Conflict Economies. Institute of Development Studies (IDS), February 2022. http://dx.doi.org/10.19088/k4d.2022.024.
Full textModlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.
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