Hybrid clustering solution selection strategy

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

64 Scopus Citations
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  • Zhiwen Yu
  • Le Li
  • Yunjun Gao
  • Jane You
  • Jiming Liu
  • Guoqiang Han

Related Research Unit(s)


Original languageEnglish
Pages (from-to)3362-3375
Journal / PublicationPattern Recognition
Issue number10
Online published18 Apr 2014
Publication statusPublished - Oct 2014


Cluster ensemble approaches make use of a set of clustering solutions which are derived from different data sources to gain a more comprehensive and significant clustering result over conventional single clustering approaches. Unfortunately, not all the clustering solutions in the ensemble contribute to the final result. In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection. Furthermore, a hybrid clustering solution selection strategy (HCSS) is designed based on a proposed weighting function, which combines several feature selection techniques for the refinement of clustering solutions in the ensemble. Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies. The experimental results on both UCI machine learning datasets and cancer gene expression profiles demonstrate that HCSS works well on most of the datasets, obtains more desirable final results, and outperforms most of the state-of-the-art clustering solution selection strategies. © 2014 Elsevier Ltd.

Research Area(s)

  • Cluster ensemble, Clustering solution selection, Feature selection, Hybrid strategy

Citation Format(s)

Hybrid clustering solution selection strategy. / Yu, Zhiwen; Li, Le; Gao, Yunjun et al.
In: Pattern Recognition, Vol. 47, No. 10, 10.2014, p. 3362-3375.

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