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Cluster Analysis Based on T ‐transitive Interval‐Valued Fuzzy Relations
Author(s) -
Wang ChingNan,
Yang MiinShen
Publication year - 2015
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21727
Subject(s) - transitive relation , vagueness , mathematics , fuzzy logic , fuzzy clustering , fuzzy set operations , relation (database) , fuzzy number , fuzzy set , data mining , interval (graph theory) , fuzzy classification , cluster analysis , defuzzification , artificial intelligence , computer science , combinatorics
In this paper, we consider cluster analysis based on T ‐transitive interval‐valued fuzzy relations. A fuzzy relation with its partitional tree for obtaining an agglomerative hierarchical clustering has been studied and applied. In general, these fuzzy‐relation‐based clustering approaches are based on real‐valued memberships of fuzzy relations. Since interval‐valued memberships may be better than real‐valued memberships to represent higher order imprecision and vagueness for human perception, in this paper we first extend fuzzy relations to interval‐valued fuzzy relations and then construct a clustering algorithm based on the proposed T ‐transitive interval‐valued fuzzy relations. We use two examples to demonstrate the efficiency and usefulness of the proposed method. In practical application, we apply the proposed clustering method to performance evaluations for academic departments of higher education by using actual engineering school data in Taiwan.