Formulations of fuzzy clustering for categorical data

被引：0
|
作者：
Umayahara, Kazutaka [1 ]
Miyamoto, Sadaaki
Nakamori, Yoshiteru
机构：
[1] Jap Adv Inst Sci & Technol, Grad Sch Knowledge Sci, Tatsunokuchi, Ishikawa 9231292, Japan
[2] Univ Tsukuba, Fac Syst & Informat Engn, Tsukuba 3058573, Japan
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2005年 / 1卷 / 01期
关键词：
fuzzy c-means; categorical data; multiset;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
New formulations of the fuzzy clustering algorithm for categorical data are proposed in this paper. Although fuzzy c-means algorithm usually uses distances from cluster centers, the distances of the memberships weighted with categorical data from the unit vectors are used in our new formulations. Different types of metrics between the weighted membership vectors and unit vectors are considered for the objective functions. Optimal solutions for the objective functions are derived and illustrative examples are given to show the obtained theoretic results.
引用
收藏
页码：83 / 94
页数：12
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