A novel validity indice for fuzzy C-means clustering algorithm

被引：0

作者：

Li, Jing ^{[1
]}

Qian, Xuezhong ^{[1
]}

机构：

[1] School of Internet of Things Engineering, Jiangnan University

来源：

Journal of Computational Information Systems | 2013年 / 9卷 / 23期

关键词：

Cluster validity indice; Compactness; Definition; Fuzzy C-means clustering; Overlap;

D O I：

10.12733/jcis8215

中图分类号：

学科分类号：

摘要：

In the paper, a novel validity indice is proposed to determine the optimal number of clusters for fuzzy clustering. The novel validity indice considers the compactness, the degree of overlapping, and fuzzy partition definition comprehensively. The compactness is recreated by adopting the new manner to measure the similarity within a cluster. The degree of overlapping measures the separation between clusters. Meanwhile, fuzzy partition definition is used to measure whether a fuzzy partition obtained is clear or not. The experimental results on six artificial data sets and a real world data set show the superior effectiveness, reliability and robustness of the proposed validity index. Copyright © 2013 Binary Information Press.

引用

页码：9679 / 9688

页数：9

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