An adaptive optimal clustering number algorithm for FKCM

Author

Guihua Chen ; Zhenlei Wang ; Xin Wang ; Feng Qian

Author_Institution

Key Lab. of Adv. Control & Optimization for Chem. Processes, East China Univ. of Sci. & Technol., Shanghai, China

Volume

1

fYear

2011

fDate

26-28 July 2011

Firstpage

65

Lastpage

69

Abstract

In order to overcome the drawbacks of fuzzy kernel clustering method (FKCM) give the clustering number in advance, sensitive to the initial cluster centers and easy to be trapped into local optimum, the adaptive algorithm for optimal clustering number of FKCM (SAICFKCM) is proposed. The proposed method uses density-based algorithm to initialize cluster centers and kernel Xie-Beni validity index to determine the optimal number of categories, to achieve unsupervised fuzzy partition of data set. The simulation experiment and the classification of naphtha attribute data verify the feasibility and effectiveness of the proposed method.

Keywords

fuzzy set theory; number theory; pattern classification; pattern clustering; statistical analysis; FKCM; SAICFKCM; adaptive optimal clustering number algorithm; cluster centers; clustering analysis; data set; density-based algorithm; fuzzy kernel clustering method; kernel Xie-Beni validity index; multivariate statistical analysis methods; naphtha attribute data classification; unsupervised pattern recognition; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Indexes; Iris; Kernel; Noise; cluster analysis; fuzzy kernel clustering; initial cluster centers; validity index;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-61284-180-9

Type

conf

DOI

10.1109/FSKD.2011.6019489

Filename

6019489