Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy

被引：0
作者：
Prem Kumar Singh
Aswani Kumar Cherukuri
Jinhai Li
机构：
[1] VIT University,School of Information Technology and Engineering
[2] Kunming University of Science and Technology,Faculty of Science
来源：
International Journal of Machine Learning and Cybernetics | 2017年 / 8卷
关键词：
Formal concept analysis; Fuzzy concept lattice; Fuzzy formal concept; Granulations; Levenshtein distance; Shannon entropy;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：179 / 189
页数：10
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