A new cluster validity criterion for the cross iterative fuzzy clustering algorithm

Author

Hou, Zhaocheng ; Wang, Jianming

Author_Institution

Dalian Univ. of Technol., China

Volume

3

fYear

2004

Firstpage

2322

Abstract

Many criteria have been proposed for clustering validity analysis in the literature. With a new definition of fuzzy compactness and fuzzy separation, a new cluster validity criterion is proposed for the cross iterative fuzzy clustering algorithm, which considers two kinds of outputs of fuzzy clustering: the geometrical properties of data and the fuzzy memberships, and has two terms: the ratio of fuzzy compactness to average fuzzy separation and the ratio of fuzzy union to fuzzy intersection of clusters. A weighting parameter is introduced to reflect the role of these two terms in validity analysis. The experimental analysis testifies that the minimum of the proposed criterion is a fine indicator for a compact and well-separated partition.

Keywords

fuzzy set theory; iterative methods; pattern clustering; cluster validity criterion; clustering validity analysis; cross iterative fuzzy clustering algorithm; fuzzy compactness; fuzzy intersection; fuzzy memberships; fuzzy separation; fuzzy union; geometrical properties; weighting parameter; Algorithm design and analysis; Clustering algorithms; Entropy; Fuzzy sets; Iterative algorithms; Partitioning algorithms; Prototypes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1342005

Filename

1342005