L1-norm based fuzzy clustering for data with tolerance

被引：0

作者：

Endo, Yasunori ^{[1
]}

Murata, Ryuichi ^{[2
]}

Toyoda, Hiromi ^{[3
]}

Miyamoto, Sadaaki ^{[1
]}

机构：

[1] Univ Tsukuba, Dept Risk Engn, Tennodai 1-1-1, Tsukuba, Ibaraki 305, Japan

[2] Univ Tsukuba, Dept Risk Engn, Grad Sch Syst & Informat Engn, Tsukuba, Ibaraki 305, Japan

[3] Univ Tsukuba, Third Cluster Coll, Coll Engn Syst, Tsukuba, Ibaraki 305, Japan

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2006年

基金：

日本学术振兴会;

关键词：

D O I：

10.1109/FUZZY.2006.1681797

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 paper, the clustering algorithms for data with tolerance are constructed based on L-1-norm and the effectiveness is verified through numerical examples. First, two objective functions, which are based on SFCM-T and EFCM-T [3], [4] respectively, is defined. It is more complex to calculate exact solutions of these functions theoretically in the L-1-norm space than the L-2-norm space (Euclidean space) so that two methods to obtain the solutions based on Refs. [1], [2] are proposed. Next, two kinds of clustering algorithms based on L-1-norm are proposed using the two methods to obtain the exact solutions. Last, the effectiveness of the proposed algorithms is verified through the numerical examples of an artificial data set and the Iris data set.

引用

页码：770 / +

页数：3

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