Knowledge representation using interval-valued fuzzy formal concept lattice

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• 作者：Prem Kumar Singh ; C. Aswani Kumar ; Jinhai Li
• 关键词：Concept lattice ; Fuzzy formal concept ; Fuzzy concept lattice ; Shannon entropy ; Interval ; valued fuzzy graph ; Interval ; valued fuzzy formal concept
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：20
• 期：4
• 页码：1485-1502
• 全文大小：856 KB
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• 作者单位：Prem Kumar Singh (1)
  C. Aswani Kumar (1)
  Jinhai Li (2)
  
  1. School of Information Technology and Engineering, VIT University, Vellore, 632014, Tamilnadu, India
  2. Faculty of Science, Kunming University of Science and Technology, Kunming, 650500, People’s Republic of China
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

Formal concept analysis (FCA) is a mathematical framework for data analysis and processing tasks. Based on the lattice and order theory, FCA derives the conceptual hierarchies from the relational information systems. From the crisp setting, FCA has been extended to fuzzy environment. This extension is aimed at handling the uncertain and vague information represented in the form of a formal context whose entries are the degrees from the scale [0, 1]. The present study analyzes the fuzziness in a given many-valued context which is transformed into a fuzzy formal context, to provide an insight into generating the fuzzy formal concepts from the fuzzy formal context. Furthermore, considering that a major problem in FCA with fuzzy setting is to reduce the number of fuzzy formal concepts thereby simplifying the corresponding fuzzy concept lattice structure, the current paper solves the problem by linking an interval-valued fuzzy graph to the fuzzy concept lattice. For this purpose, we propose an algorithm for generating the interval-valued fuzzy formal concepts. To measure the weight of fuzzy formal concepts, an algorithm is proposed using Shannon entropy. The knowledge represented by formal concepts using interval-valued fuzzy graph is compared with entropy-based-weighted fuzzy concepts at chosen threshold.