Resampling-based selective clustering ensembles

Yi HONG, Sam KWONG, Hanli WANG, Qingsheng REN

Research output: Journal PublicationsJournal Article (refereed)peer-review

50 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)298-305
JournalPattern Recognition Letters
Issue number3
Publication statusPublished - 1 Feb 2009
Externally publishedYes

Bibliographical note

The work was partially supported by City University Strategic Grant No. 7002294 and a grant from the Research Grants Council of Hong Kong Special Administrative Region, China Project No. 9041236/CityU 114707. The authors would like to thank the comments and suggestions from the reviewers.


  • Clustering analysis
  • Clustering ensembles
  • Resampling technique


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