Power-Regularized Fuzzy Clustering for Spherical Data
Author(s) -
Yuchi Kanzawa
Publication year - 2018
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2018.p0163
Subject(s) - fuzzy clustering , cluster analysis , flame clustering , data mining , computer science , regularization (linguistics) , fuzzy logic , pattern recognition (psychology) , fuzzy number , artificial intelligence , fuzzy set , fuzzy classification , mathematics , cure data clustering algorithm , algorithm
In this paper, a power-regularization-based fuzzy clustering method is proposed for spherical data. Power regularization has not been previously applied to fuzzy clustering for spherical data. The proposed method is transformed to the conventional fuzzy clustering method, entropy-regularized fuzzy clustering for spherical data (eFCS), for a specified fuzzification parameter value. Numerical experiments on two artificial datasets reveal the properties of the proposed method. Furthermore, numerical experiments on four real datasets indicate that this method is more accurate than the conventional fuzzy clustering methods: standard fuzzy clustering for spherical data (sFCS) and eFCS.
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