Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)175-188
Journal / PublicationPattern Recognition
Volume37
Issue number2
Publication statusPublished - Feb 2004

Abstract

The self-organizing map (SOM) has been widely used in many industrial applications. Classical clustering methods based on the SOM often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. In this paper, through some preprocessing techniques for filtering out noises and outliers, we propose a new two-level SOM-based clustering algorithm using a clustering validity index based on inter-cluster and intra-cluster density. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data better than the classical clustering algorithms based on the SOM, and find an optimal number of clusters. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Research Area(s)

  • Clustering validity index, Hierarchical clustering, Multi-representation, Partitioning clustering, Self-organizing map