Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

SMART: A subspace clustering algorithm that automatically identifies the appropriate number of clusters

Language

English

Abstract

This paper presents a subspace κ-means clustering algorithm for high-dimensional data with automatic selection of κ. A new penalty term is introduced to the objective function of the fuzzy κ-means clustering process to enable several clusters to compete for objects, which leads to merging some cluster centres and the identification of the 'true' number of clusters. The algorithm determines the number of clusters in a dataset by adjusting the penalty term factor. A subspace cluster validation index is proposed and employed to verify the subspace clustering results generated by the algorithm. The experimental results from both the synthetic and real data have demonstrated that the algorithm is effective in producing consistent clustering results and the correct number of clusters. Some real datasets are used to demonstrate how the proposed algorithm can determine interesting sub-clusters in the datasets. Copyright © 2009 Inderscience Enterprises Ltd.

Keywords

κ-means, Cluster numbers, Data mining, Subspace clustering, Weighting

Publication Date

2009

Source Publication Title

International Journal of Data Mining, Modelling and Management

Volume

1

Issue

2

Start Page

149

End Page

177

Publisher

Inderscience

DOI

10.1504/IJDMMM.2009.026074

Link to Publisher's Edition

http://dx.doi.org/10.1504/IJDMMM.2009.026074

ISSN (print)

17591163

ISSN (electronic)

17591171

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