Generalization Property of Fuzzy Classification Function for Tsallis Entropy-Regularization of Bezdek-Type Fuzzy C-Means Clustering

被引：0
|
作者：
Kanzawa, Yuchi [1 ]
机构：
[1] Shibaura Inst Technol, Tokyo, Japan
来源：
MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE (MDAI 2020) | 2020年 / 12256卷
关键词：
MAXIMIZING MODEL;
D O I：
10.1007/978-3-030-57524-3_10
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, Tsallis entropy-regularized Bezdek-type fuzzy c-means clustering method is proposed. Because the proposed method reduces to four conventional fuzzy clustering methods by appropriately controlling fuzzification parameters, the proposed method is considered to be their generalization. Through numerical experiments, this generalization property is confirmed; in addition, it is observed that the fuzzy classification function of the proposed method approaches a value equal to the reciprocal of the cluster number.
引用
收藏
页码：119 / 131
页数：13
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