Fuzzy cluster validation index based on inter-cluster proximity

被引：69

作者：

Kim, DW

Lee, KH

Lee, D

机构：

[1] Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Taejon 305701, South Korea

[2] Korea Adv Inst Sci & Technol, Dept BioSyst, Taejon 305701, South Korea

来源：

PATTERN RECOGNITION LETTERS | 2003年 / 24卷 / 15期

关键词：

cluster validity; proximity measure; fuzzy clustering; fuzzy C-means;

D O I：

10.1016/S0167-8655(03)00101-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

A new cluster validity index is proposed for fuzzy partitions obtained from Fuzzy C-Means algorithm. The proposed validity index exploits an inter-cluster proximity between fuzzy clusters. The inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value indicates well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing the inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index. (C) 2003 Elsevier B.V. All rights reserved.

引用

页码：2561 / 2574

页数：14

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