Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy
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- 作者：Prem Kumar Singh ; Aswani Kumar Cherukuri…
- 关键词：Formal concept analysis ; Fuzzy concept lattice ; Fuzzy formal concept ; Granulations ; Levenshtein distance ; Shannon entropy
- 刊名：International Journal of Machine Learning and Cybernetics
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：8
- 期：1
- 页码：179-189
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition;
- 出版者：Springer Berlin Heidelberg
- ISSN：1868-808X
- 卷排序：8
文摘
In this paper we propose a method for reducing the number of formal concepts in formal concept analysis of data with fuzzy attributes. We compute the weight of fuzzy formal concepts based on Shannon entropy. Further, the number of fuzzy formal concepts is reduced at chosen granulation of their computed weight. We show that the results obtained from the proposed method are in good agreement with Levenshtein distance method and interval–valued fuzzy formal concepts method but with less computational complexity.