Relevant bibliographies by topics / Fuzzy relational concept analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Fuzzy relational concept analysis'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Fuzzy relational concept analysis"

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Boffa, Stefania, Petra Murinová, and Vilém Novák. "A proposal to extend Relational Concept Analysis with fuzzy scaling quantifiers." Knowledge-Based Systems 231 (November 2021): 107452. http://dx.doi.org/10.1016/j.knosys.2021.107452.

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De Maio, C., G. Fenza, M. Gallo, V. Loia, and S. Senatore. "Formal and relational concept analysis for fuzzy-based automatic semantic annotation." Applied Intelligence 40, no. 1 (June 13, 2013): 154–77. http://dx.doi.org/10.1007/s10489-013-0451-7.

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Nobuhara, Hajime, and Kaoru Hirota. "A Fuzzification of Morphological Wavelets Based on Fuzzy Relational Calculus and its Application to Image Compression/Reconstruction." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 373–78. http://dx.doi.org/10.20965/jaciii.2004.p0373.

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A new style of fuzzy wavelets is proposed by the fuzzification of morphological wavelets. Due to the correspondence of the morphological wavelets operations and fuzzy relational ones, wavelets analysis/synthesis schemes can be formulated based on fuzzy relational calculus. To enable efficient image compression/reconstruction, the concept of the alpha-band which is an alpha-cut generalization, is also proposed for thresholding wavelets. In an image compression/reconstruction experiment using test images extracted from the Standard Image DataBAse (SIDBA), it is confirmed that the root mean square error (RMSE) of the proposed soft thresholding is decreased to 87.3% of conventional hard thresholding, when the original image is "Lenna."
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ALCALDE, CRISTINA, ANA BURUSCO, and RAMÓN FUENTES-GONZÁLEZ. "ANALYSIS OF CERTAIN L-FUZZY RELATIONAL EQUATIONS AND THE STUDY OF ITS SOLUTIONS BY MEANS OF THE L-FUZZY CONCEPT THEORY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 01 (February 2012): 21–40. http://dx.doi.org/10.1142/s021848851250002x.

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Given a Brouwerian complete lattice (L,≤) and two referential sets K and E, and using a fuzzy relation R ∈ LE×E which is reflexive and symmetric, certain fuzzy relations [Formula: see text] are characterized as solutions of X ⊲ R = X, proving that they play the role of the blocks in the context of crisp tolerance relations. Moreover, it is verified that these new fuzzy blocks [Formula: see text] can be determinated by means of the L-fuzzy concepts associated with the K -labeled L-fuzzy context (L,K,E,E,R).
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Močkoř, Jiří. "Cut Systems with Relational Morphisms for Semiring-Valued Fuzzy Structures." Axioms 12, no. 2 (February 2, 2023): 153. http://dx.doi.org/10.3390/axioms12020153.

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Many of the new MV-valued fuzzy structures, including intuitionistic, neutrosophic, or fuzzy soft sets, can be transformed into so-called almost MV-valued fuzzy sets, or, equivalently, fuzzy sets with values in dual pair of semirings (in symbols, (R,R*)-fuzzy sets). This transformation allows any construction of almost MV-valued fuzzy sets to be retransformed into an analogous construction for these new fuzzy structures. In that way, approximation theories for (R,R*)-fuzzy sets, rough (R,R*)-fuzzy sets theories, or F-transform theories for (R,R*)-fuzzy sets have already been created and then retransformed for these new fuzzy structures. In this paper, we continue this trend and define, on the one hand, the theory of extensional (R,R*)-fuzzy sets defined on sets with fuzzy similarity relations with values in dual pair of semirings and power sets functors related to this theory and, at the same time, the theory of cuts with relational morphisms of these structures. Illustratively, the reverse transformations of some of these concepts into new fuzzy structures are presented.
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Jiang, Xian-Ling, and Yi-Lin Zhao. "Grey Relational Method for Evaluating the Macro-Economy Performance with Triangular Fuzzy Information." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7385–89. http://dx.doi.org/10.1166/jctn.2016.5730.

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The new theory comes when the current economic circumstance cannot be explained. The Great Depression in 1930s severely smashed the world economy and no explanations and policies were provided by the classical laissez-faire until the Roosevelt New Deal took the world economy out of depression. The macroeconomic theory has been brought out by John Maynard Keynes. Early this century, not only did the US Sub-prime crisis strongly affect the world economy, but also the macroeconomic theory. The development of the macroeconomic theory after the Sub-prime crisis becomes a hot topic. In this paper, we investigate the multiple attribute decision making (MADM) problems for evaluating the macro-economy performance with triangular fuzzy information. Then, we extend the grey relational analysis (GRA) procedure for triangular fuzzy multiple attribute decision making for evaluating the macro-economy performance in triangular fuzzy setting. According to the concept of the GRA, a fuzzy relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of fuzzy grey relational coefficient to both the triangular fuzzy positive-ideal solution (TFPIS) and triangular fuzzy negative-ideal solution (TFNIS) simultaneously. Finally, an illustrative example for evaluating the macro-economy performance is given to verify the developed approach and to demonstrate its practicality and effectiveness.
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Huang, Sue-Fen. "Cognitive diagnostic assessment based on knowledge structure." MATEC Web of Conferences 169 (2018): 01020. http://dx.doi.org/10.1051/matecconf/201816901020.

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The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. In order to insight the misconception of learning basic mathematics and progress teaching. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modelling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. In this paper, some well-known knowledge structure assessment methods will be discussed. For item connection, Bart et al ordering theory and Takeya’s item relational structure provided ordering coefficient to construct item relationships and hierarchies. For concepts or skills connection, Warfield’s ISM and Lin et al Concept Advanced Interpretive Structural Modelling (CAISM) provided to construct graphic relationship among elements and display the individualized concept hierarchy structure by numeric and picture. Samples contain 427 which come from Min-Hwei Junior College. Subjects were analyzed by CAISM. It shows the traditional assessment is not the only criteria; it must be combined with other assessment tools. The result shows that CAISM gives meaningful learning and lacks of learners.
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Wei, Qian. "Product Shape Design Scheme Evaluation Method Based on Spatial Data Mining." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/3231357.

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The stage of product modeling design implies a lot of complex tacit knowledge, which is the embodiment of the design concept centered on product modeling design and is also the hot spot and difficulty of modern design theory and method research. Aiming at the evaluation and decision of product modeling design scheme, a decision-making method of approaching ideal solution ranking based on grey relational analysis was proposed, which realized the convergence of tacit knowledge. The empty association rule is an important knowledge content of spatial data mining. A fuzzy genetic algorithm can solve the characteristics of random and nonlinear problems and solve the data mining problems of spatial association rules. The fuzzy genetic algorithm of discrete crossover probability and mutation probability is applied to data mining of spatial association rules in a spatial database, the coding method of the fuzzy genetic algorithm and the construction of fitness function are discussed, and the process of mining spatial association rules is given. The results show that the method of mining s association rules with the fuzzy genetic algorithm is feasible and has higher mining efficiency. This paper discusses the construction method of designing a decision support database based on linear regression and neural network and then proposes a decision method combining TOPSIS and grey relational analysis, which comprehensively considers the position and shape of the scheme data curve.
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Riaz, Muhammad, Muhammad Tahir Hamid, Deeba Afzal, Dragan Pamucar, and Yu-Ming Chu. "Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets." PLOS ONE 16, no. 2 (February 25, 2021): e0246485. http://dx.doi.org/10.1371/journal.pone.0246485.

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q-Rung orthopair fuzzy set (qROFS) and m-polar fuzzy set (mPFS) are rudimentary concepts in the computational intelligence, which have diverse applications in fuzzy modeling and decision making under uncertainty. The aim of this paper is to introduce the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) as a hybrid model of q-rung orthopair fuzzy set and m-polar fuzzy set. A qROmPFS has the ability to deal with real life situations when decision experts are interested to deal with multi-polarity as well as membership and non-membership grades to the alternatives in an extended domain with q-ROF environment. Certain operations on qROmPFSs and several new notions like support, core, height, concentration, dilation, α-cut and (α, β)-cut of qROmPFS are defined. Additionally, grey relational analysis (GRA) and choice value method (CVM) are presented under qROmPFSs for multi-criteria decision making (MCDM) in robotic agri-farming. The proposed methods are suitable to find out an appropriate mode of farming among several kinds of agri-farming. The applications of proposed MCDM approaches are illustrated by respective numerical examples. To justify the feasibility, superiority and reliability of proposed techniques, the comparison analysis of the final ranking in the robotic agri-farming computed by the proposed techniques with some existing MCDM methods is also given.
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Rybanov, Alexander Aleksandrovich. "A PRACTICAL METHOD FOR IMPLEMENTING FUZZY QUERIES FOR RELATIONAL DATABASES." Mathematics and Informatics LXV, no. 4 (August 30, 2022): 379–92. http://dx.doi.org/10.53656/math2022-4-5-pra.

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Information systems that use databases are flexible to the extent that they allow users to request the data they need. SQL is limited to precise data processing and does not directly express fuzzy concepts of natural language. Therefore, giving SQL some flexibility can help users improve interaction with information systems without requiring them to learn a completely new language. The task of reducing the labor intensity of the process of integrating the mechanisms of fuzzy requests to existing information systems is urgent. The article shows the limitations of clear queries, considers various forms of fuzzy queries. Known approaches to implementing fuzzy queries to relational databases were analyzed. Provides a detailed analysis of fuzzy queries, as well as their conversion to standard SQL queries using MySQL. The proposed method for implementing the ability to work with fuzzy queries is based on expanding a clear database with stored functions, without changing the structure and composition of its tables. The advantages of the method are: increased readability and understanding of SQL queries; ease of integration with existing databases of information systems; flexible adjustment of the linguistic variable membership function in accordance with the needs of the database user. The application of the method is shown by the example of adapting a MySQL database. The proposed adaptation method can be widely used to implement fuzzy queries to databases of various DBMS that support work with stored functions.
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Dissertations / Theses on the topic "Fuzzy relational concept analysis"

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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.

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2012 - 2013
The 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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Nica, Cristina. "Exploring sequential data with relational concept analysis." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAD032/document.

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De nombreuses méthodes d’extraction de motifs séquentiels ont été proposées pour découvrir des motifs utiles qui décrivent les données analysées. Certaines de ces travaux se sont concentrés sur l’énumération efficace de motifs partiellement ordonnés fermés (cpo-motifs), ce qui rend leur évaluation difficile pour les experts, car leur nombre peut être important. Par suite, nous proposons une approche nouvelle, qui consiste à extraire directement des cpo-motifs multi-niveaux qui sont organisés dans une hiérarchie. Nous proposons une méthode originale dans la cadre de l’Analyse Relationnelle de Concepts (ARC), appelée RCA-SEQ, qui exploite la structure et les propriétés des treillis issus de l’ARC. RCA-SEQ comporte cinq étapes : le prétraitement des données ; l'exploration par l’ARC des données ; l'extraction automatisée d'une hiérarchie de cpo-motifs multi-niveaux par navigation des treillis issus de l’ARC ; la sélection de cpo-motifs pertinents ; l'évaluation des motifs par les experts
Many sequential pattern mining methods have been proposed to discover useful patterns that describe the analysed sequential data. Several of these works have focused on efficiently enumerating all closed partially-ordered patterns (cpo-patterns), that makes their evaluation a laboured task for experts since their number can be large. To address this issue, we propose a new approach, that is to directly extract multilevel cpo-patterns implicitly organised into a hierarchy. To this end, we devise an original method within the Relational Concept Analysis (RCA) framework, referred to as RCA-SEQ, that exploits the structure and properties of the lattices from the RCA output. RCA-SEQ spans five steps: the preprocessing of the raw data; the RCA-based exploration of the preprocessed data; the automatic extraction of a hierarchy of multilevel cpo-patterns by navigating the lattices from the RCA output; the selection of relevant multilevel cpo-patterns; the pattern evaluation done by experts
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De, Maio Carmen. "Fuzzy concept analysis for semantic knowledge extraction." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/1307.

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2010 - 2011
Availability 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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Kanade, Parag M. "Fuzzy ants as a clustering concept." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.

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Rudolph, Sebastian. "Relational Exploration: Combining Description Logics and Formal Concept Analysis for Knowledge Specification." Doctoral thesis, Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A25002.

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Facing the growing amount of information in today's society, the task of specifying human knowledge in a way that can be unambiguously processed by computers becomes more and more important. Two acknowledged fields in this evolving scientific area of Knowledge Representation are Description Logics (DL) and Formal Concept Analysis (FCA). While DL concentrates on characterizing domains via logical statements and inferring knowledge from these characterizations, FCA builds conceptual hierarchies on the basis of present data. This work introduces Relational Exploration, a method for acquiring complete relational knowledge about a domain of interest by successively consulting a domain expert without ever asking redundant questions. This is achieved by combining DL and FCA: DL formalisms are used for defining FCA attributes while FCA exploration techniques are deployed to obtain or refine DL knowledge specifications.
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Konecny, Jan. "Isotone fuzzy Galois connections and their applications in formal concept analysis." Diss., Online access via UMI:, 2009.

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Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009.
Includes bibliographical references.
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Rudolph, Sebastian [Verfasser]. "Relational exploration : combining description logics and formal concept analysis for knowledge specification / von Sebastian Rudolph." Karlsruhe : Univ.-Verl. Karlsruhe, 2007. http://d-nb.info/983756430/34.

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Rudolph, Sebastian. "Relational Exploration." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2007. http://nbn-resolving.de/urn:nbn:de:swb:14-1172682174599-12286.

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Facing the growing amount of information in today's society, the task of specifying human knowledge in a way that can be unambiguously processed by computers becomes more and more important. Two acknowledged fields in this evolving scientific area of Knowledge Representation are Description Logics (DL) and Formal Concept Analysis (FCA). While DL concentrates on characterizing domains via logical statements and inferring knowledge from these characterizations, FCA builds conceptual hierarchies on the basis of present data. This work introduces Relational Exploration, a method for acquiring complete relational knowledge about a domain of interest by successively consulting a domain expert without ever asking redundant questions. This is achieved by combining DL and FCA: DL formalisms are used for defining FCA attributes while FCA exploration techniques are deployed to obtain or refine DL knowledge specifications.
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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.

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With the growing number of large data sets, the necessity of complexity reduction applies today more than ever before. Moreover, some data may also be vague or uncertain. Thus, whenever we have an instrument for data analysis, the questions of how to apply complexity reduction methods and how to treat fuzzy data arise rather naturally. In this thesis, we discuss these issues for the very successful data analysis tool Formal Concept Analysis. In fact, we propose different methods for complexity reduction based on qualitative analyses, and we elaborate on various methods for handling fuzzy data. These two topics split the thesis into two parts. Data reduction is mainly dealt with in the first part of the thesis, whereas we focus on fuzzy data in the second part. Although each chapter may be read almost on its own, each one builds on and uses results from its predecessors. The main crosslink between the chapters is given by the reduction methods and fuzzy data. In particular, we will also discuss complexity reduction methods for fuzzy data, combining the two issues that motivate this thesis
Komplexitätsreduktion 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
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Kandasamy, Meenakshi. "Approaches to Creating Fuzzy Concept Lattices and an Application to Bioinformatics Annotations." Miami University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=miami1293821656.

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Books on the topic "Fuzzy relational concept analysis"

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J, Skolnick Neil, and Warshaw Susan C, eds. Relational perspectives in psychoanalysis. Hillsdale, NJ: Analytic Press, 1992.

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Ciappei, Cristiano, and Massimiliano Pellegrini, eds. Facility management for global care. Florence: Firenze University Press, 2010. http://dx.doi.org/10.36253/978-88-6453-088-8.

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The aim of this work is to bring the study of facility management, that is the management of the services connected with the maintenance and valorisation of real estate, to a higher and more complete level. We have sought to overcome the – albeit inevitable – engineering/efficientist approach, to arrive at an all-round promotion and analysis of the discipline, hinging on the concept of service. This means, first and foremost, rediscovering the relational aspect apropos the clientele and, starting from this, moving towards a restructuring of the service where the aim is to meet personal requirements rather than purely technical standards. The aspiration, underscored in the title, is in fact that of arriving at a "global care" of the person.
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Skolnick, Neil J., and Susan C. Warshaw. Relational Perspectives in Psychoanalysis. Taylor & Francis Group, 2015.

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Skolnick, Neil J., and Susan C. Warshaw. Relational Perspectives in Psychoanalysis. Taylor & Francis Group, 2015.

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Skolnick, Neil J., and Susan C. Warshaw. Relational Perspectives in Psychoanalysis. Taylor & Francis Group, 2015.

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Skolnick, Neil J., and Susan C. Warshaw. Relational Perspectives in Psychoanalysis. Taylor & Francis Group, 2015.

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Gross, Justin H., and Joshua M. Jansa. Relational Concepts, Measurement, and Data Collection. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.7.

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Political phenomena are inherently relational, so it is natural that network analysis should come to play an important role in the study of politics. And yet relational data present special practical and methodological problems. The network data scholars would like to collect are often incomplete or altogether inaccessible. It is tempting to take whatever data are available and treat these as a proxy for the desired variables. This chapter reviews the most prominent relational concepts in political science and the operationalization strategies and data collection techniques typically employed. It then examines common practices for handling missing data and identifies recent innovations in this area. Finally, the chapter recommends that political scientists give more consideration to the concept development and measurement phases of research design and proposes possible directions for the development of network measurement models.
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Schneider, Volker. Hugh Heclo, “Issue Networks and the Executive Establishment”. Edited by Martin Lodge, Edward C. Page, and Steven J. Balla. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199646135.013.28.

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This chapter comments on Hugh Heclo’s 1978 paper “Issue Networks and the Executive Establishment,” an innovative analysis of modern politics based on four analytical perspectives of public policy: an actor-centered or agent-based dynamic perspective, a relational perspective, a cultural or cognitive perspective, and a long-term perspective. Widely regarded as a classic in policy analysis and public administration, Heclo’s paper uses the concept of “issue networks” to describe the highly intricate and diversified webs of influence that shape modern American policy-making. This chapter discusses Heclo’s concept of issue networks within the context of the American situation in government and public administration, as well as its impact on fields such as political science.
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Morphy, Howard. Art as Action, Art as Evidence. Edited by Dan Hicks and Mary C. Beaudry. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199218714.013.0011.

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This article is a strong defence of the idea of ‘art’, but it also recognizes its complexity and the fact that as a concept, ‘art’ is fuzzy around the edges. It uses a concept of family resemblance and sees art objects as forming polythetic sets. The category contains within it an immense diversity and includes objects that have little in common with each other and require very different methods of analysis. However, at the heart of this concept of art lies a set of loosely connected features or themes around which the idea of art coalesces: art is a form of action, art production is integral to meaning creating processes and requires a sense of form, and art is associated with aesthetic experience. This article proceeds to explain ideas of art and material culture. An analysis of art as cross-cultural category concludes this article.
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Rushton, Cynda Hylton. Conceptualizing Moral Resilience. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190619268.003.0007.

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Moral resilience, the ability of an individual to preserve or restore integrity in response to moral adversity, draws on targeted scholarship of the broader concept of resilience in other contexts. This chapter builds on definitions in the literature and qualitative analysis of clinicians’ definitions of moral resilience in order to outline the key attributes of moral resilience. The foundation of moral resilience is personal and relational integrity. The attributes of self-regulation and self-awareness, such as mindfulness, buoyancy, moral efficacy, and self-stewardship, support the preservation or restoration of integrity. These attributes are defined and illustrated with quotes from clinicians. Taken together, these attributes constitute a conceptual basis for moral resilience.
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Book chapters on the topic "Fuzzy relational concept analysis"

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Šostak, Alexander, and Ingrīda Uļjane. "Fuzzy Relations: The Fundament for Fuzzy Rough Approximation, Fuzzy Concept Analysis and Fuzzy Mathematical Morphology." In Computational Intelligence and Mathematics for Tackling Complex Problems 4, 25–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07707-4_4.

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Bělohlávek, Radim. "Object-Attribute Fuzzy Relations and Fuzzy Concept Lattices." In Fuzzy Relational Systems, 215–72. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0633-1_5.

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Bazin, Alexandre, Jessie Carbonnel, Marianne Huchard, Giacomo Kahn, Priscilla Keip, and Amirouche Ouzerdine. "On-demand Relational Concept Analysis." In Formal Concept Analysis, 155–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21462-3_11.

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Kötters, Jens. "Object Configuration Browsing in Relational Databases." In Formal Concept Analysis, 151–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20514-9_13.

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Cabrera, Inma P., Pablo Cordero, Emilio Muñoz-Velasco, and Manuel Ojeda-Aciego. "A Relational Extension of Galois Connections." In Formal Concept Analysis, 290–303. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21462-3_19.

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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.

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Bělohlávek, 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.

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Dolques, Xavier, Florence Le Ber, Marianne Huchard, and Clémentine Nebut. "Relational Concept Analysis for Relational Data Exploration." In Advances in Knowledge Discovery and Management, 57–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23751-0_4.

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Rouane-Hacene, Mohamed, Marianne Huchard, Amedeo Napoli, and Petko Valtchev. "Soundness and Completeness of Relational Concept Analysis." In Formal Concept Analysis, 228–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38317-5_15.

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Brito, 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.

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Conference papers on the topic "Fuzzy relational concept analysis"

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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.

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de Lemos, Francisco Luiz, Karl-Heinz Helmuth, and Terry Sullivan. "Transparent Tools for Uncertainty Analysis in High Level Waste Disposal Facilities Safety." In The 11th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2007. http://dx.doi.org/10.1115/icem2007-7277.

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In this paper some results of a further development of a technical cooperation project, initiated in 2004, between the CDTN/CNEN, The Brazilian National Nuclear Energy Commission, and the STUK, The Finnish Radiation and Nuclear Safety Authority, are presented. The objective of this project is to study applications of fuzzy logic, and artificial intelligence methods, on uncertainty analysis of high level waste disposal facilities safety assessment. Uncertainty analysis is an essential part of the study of the complex interactions of the features, events and processes, which will affect the performance of the HLW disposal system over the thousands of years in the future. Very often the development of conceptual and computational models requires simplifications and selection of over conservative parameters that can lead to unrealistic results. These results can mask the existing uncertainties which, consequently, can be an obstacle to a better understanding of the natural processes. A correct evaluation of uncertainties and their rule on data interpretation is an important step for the improvement of the confidence in the calculations and public acceptance. This study focuses on dissolution (source), solubility and sorption (sink) as key processes for determination of release and migration of radionuclides. These factors are affected by a number of parameters that characterize the near and far fields such as pH; temperature; redox conditions; and other groundwater properties. On the other hand, these parameters are also consequence of other processes and conditions such as water rock interaction; pH and redox buffering. Fuzzy logic tools have been proved to be suited for dealing with interpretation of complex, and some times conflicting, data. For example, although some parameters, such as pH and carbonate, are treated as independent, they have influence in each other and on the solubility. It is used the technique of fuzzy cognitive mapping is used for analysis of effects of variations on one parameter on the others in a system. This technique uses the concept of fuzzy sets to represent the “quality” of the relation between parameters rather then deterministic numbers.
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Chen, Shyi-Ming, and Yonathan Randyanto. "Concept representation in intuitionistic fuzzy social relational networks." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974006.

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Feng Jiang, Youxin Meng, and Yun Liu. "Formal concept analysis in relational contexts." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664730.

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Mateiu, Patricia, Adrian Groza, and Cristina Nica. "Learning Ontologies with Relational Concept Analysis." In 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2022. http://dx.doi.org/10.1109/sami54271.2022.9780837.

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Begam, M. Farida. "Domain Ontology Construction using Formal Concept and Relational Concept Analysis." In 2021 2nd Global Conference for Advancement in Technology (GCAT). IEEE, 2021. http://dx.doi.org/10.1109/gcat52182.2021.9587855.

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Hacene, Mohamed Rouane, Amedeo Napoli, Petko Valtchev, Yannick Toussaint, and Rokia Bendaoud. "Ontology Learning from Text Using Relational Concept Analysis." In 2008 International MCETECH Conference on e-Technologies. IEEE, 2008. http://dx.doi.org/10.1109/mcetech.2008.29.

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Tiwari, Ashutosh, Q. M. Danish Lohani, and Pranab K. Muhuri. "Intuitionistic Fuzzy Grey Relational Analysis Sorting Technique." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882547.

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Chen, Zhijie, Weizhen Chen, Qile Chen, and Mian-Yun Chen. "Trend Relational Analysis and Grey-Fuzzy Clustering Method." In Proceedings of the 2006 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.21.

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Gao, Zhi-Yong, Yong-Quan Liang, and Shu-Han Qiao. "Relational Database Ontology Discovery Method Based on Formal Concept Analysis." In 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/mme-16.2017.101.

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Reports on the topic "Fuzzy relational concept analysis"

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Baader, 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.

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Formal Concept Analysis (FCA) can be used to analyze data given in the form of a formal context. In particular, FCA provides efficient algorithms for computing a minimal basis of the implications holding in the context. In this paper, we extend classical FCA by considering data that are represented by relational structures rather than formal contexts, and by replacing atomic attributes by complex formulae defined in some logic. After generalizing some of the FCA theory to this more general form of contexts, we instantiate the general framework with attributes defined in the Description Logic (DL) EL, and with relational structures over a signature of unary and binary predicates, i.e., models for EL. In this setting, an implication corresponds to a so-called general concept inclusion axiom (GCI) in EL. The main technical result of this report is that, in EL, for any finite model there is a finite set of implications (GCIs) holding in this model from which all implications (GCIs) holding in the model follow.
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