Resampling-based selective clustering ensembles

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

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Detail(s)

Original languageEnglish
Pages (from-to)298-305
Journal / PublicationPattern Recognition Letters
Volume30
Issue number3
Publication statusPublished - 1 Feb 2009

Abstract

Traditional clustering ensembles methods combine all obtained clustering results at hand. However, we observe that it can often achieve a better clustering solution if only part of all available clustering results are combined. This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. The proposed selective clustering ensembles method works by evaluating the qualities of all obtained clustering results through resampling technique and selectively choosing part of promising clustering results to build the ensemble committee. The final solution is obtained through combining the clustering results of the ensemble committee. Experimental results on several real data sets demonstrate that resampling-based selective clustering ensembles method is often able to achieve a better solution when compared with traditional clustering ensembles methods. © 2008 Elsevier B.V. All rights reserved.

Research Area(s)

  • Clustering analysis, Clustering ensembles, Resampling technique

Citation Format(s)

Resampling-based selective clustering ensembles. / Hong, Yi; Kwong, Sam; Wang, Hanli et al.
In: Pattern Recognition Letters, Vol. 30, No. 3, 01.02.2009, p. 298-305.

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