Fuzzy formal concept analysis

CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFormal Context Analysis is a mathematical theory that enables us to find concepts from a given set of objects, a set of attributes and a relation on them. There is a hierarchy of such concepts, from which a complete lattice can be made. In this paper we p831192205CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIOR306546/2017-5308524/2014-437th Conference of the North-American-Fuzzy-Information-Processing-Society (NAFIPS

Extending FuzAtAnalyzer to approach the management of classical negation

FuzAtAnalyzer was conceived as a Java framework which goes beyond of classical tools in formal concept analysis. Specifically, it successfully incorporated the management of uncertainty by means of methods and tools from the area of fuzzy formal concept analysis. One limitation of formal concept analysis is that they only consider the presence of properties in the objects (positive attributes) as much in fuzzy as in crisp case. In this paper, a first step in the incorporation of negations is presented. Our aim is the treatment of the absence of properties (negative attributes). Specifically, we extend the framework by including specific tools for mining knowledge combining crisp positive and negative attributes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Activating Generalized Fuzzy Implications from Galois Connections

This paper deals with the relation between fuzzy implications and Galois connections, trying to raise the awareness that the fuzzy implications are indispensable to generalise Formal Concept Analysis. The concrete goal of the paper is to make evident that Galois connections, which are at the heart of some of the generalizations of Formal Concept Analysis, can be interpreted as fuzzy incidents. Thus knowledge processing, discovery, exploration and visualization as well as data mining are new research areas for fuzzy implications as they are areas where Formal Concept Analysis has a niche.F.J. Valverde-Albacete—was partially supported by EU FP7 project LiMoSINe, (contract 288024). C. Peláez-Moreno—was partially supported by the Spanish Government-CICYT project 2011-268007/TEC.Publicad

Comparison of formulations of applied tasks with intervals, fuzzy sets and probability approaches

The focus of this paper is to clarify the concepts of solutions in linear equations in interval, probabilistic and fuzzy sets setting for real word tasks. There is a fundamental difference between formal definitions of the solutions and physically meaningful concept of solution in applied tasks when equations have uncertain components. For instance, a formal definition of the solution in terms of Moore interval analysis can be completely irrelevant for solving a real world task. We show that formal definitions must follow meaningful concept of the solution in the real world. The paper proposed several formalized definitions of the concept of solution for the linear equations with uncertain components in the interval, probability and fuzzy sets term

Interpretation of Fuzzy Attribute Subsets in Generalized One-Sided Concept Lattices

In this paper we describe possible interpretation and reduction of fuzzy attributes in Generalized One-sided Concept Lattices (GOSCL). This type of concept lattices represent generalization of Formal Concept Analysis (FCA) suitable for analysis of datatables with different types of attributes. FCA as well as generalized one-sided concept lattices represent conceptual data miningmethods. With growing number of attributes the interpretation of fuzzy subsets may become unclear, hence another interpretation of this fuzzy attribute subsets can be valuable. The originality of the presented method is based on the usage of one-sided concept lattices derived from submodels of former object-attribute model by grouping attributes with the same truth value structure. This leads to new method for attribute reduction in GOSCL environment

FUZZY FORMAL CONCEPT ANALYSIS UNTUK KEMIRIPAN DOKUMEN

ABSTRAK Fuzzy logic dapat dimasukkan ke dalam ontologi untuk representasi ketidakpastian informasi yang ditemukan di banyak aplikasi domain karena kurangnya jelas batas-batas antara konsep domain. Fuzzy ontologi dihasilkan dari konsep hirarki yang telah ditetapkan. Namun, untuk membangun sebuah konsep hirarki untuk domain tertentu dapat menjadi tugas yang sulit dan membosankan. Untuk mengatasi masalah ini, diusulkan Fuzzy Formal Concept Analysis(FFCA). Titik awal dari metode diusulkan dalam paper ini adalah definisi dari konteks , relasi kemiripan pada domain ontologi kemudian memetakan ke dalam concept lattice. Dengan penggunaan tool lattice navigator,metode yang diusulkan mampu mengelompokkan domain ontology secara efektif. Kata Kunci: Ontology, Formal Concept Analysis, Fuzzy Formal Concept Analysis,konsep Lattic