Learning assignment order of instances for the constrained K-means clustering algorithm

Yi HONG*, Sam KWONG

*Corresponding author for this work

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

40 Citations (Scopus)

Abstract

The sensitivity of the constrained K-means clustering algorithm (Cop-Kmeans) to the assignment order of instances is studied, and a novel assignment order learning method for Cop-Kmeans, termed as clustering Uncertainty-based Assignment order Learning Algorithm (UALA), is proposed in this paper. The main idea of UALA is to rank all instances in the data set according to their clustering uncertainties calculated by using the ensembles of multiple clustering algorithms. Experimental results on several real data sets with artificial instance-level constraints demonstrate that UALA can identify a good assignment order of instances for Cop-Kmeans. In addition, the effects of ensemble sizes on the performance of UALA are analyzed, and the generalization property of Cop-Kmeans is also studied.

Original languageEnglish
Pages (from-to)568-574
Number of pages7
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume39
Issue number2
Early online date22 Dec 2008
DOIs
Publication statusPublished - Apr 2009
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the Hong Kong RGC Competitive Earmarked Research Grant Project 9041236 (CityU 114707). This paper was recommended by Associate Editor R. Lynch.

Keywords

  • Constrained K-means clustering algorithm (Cop-Kmeans)
  • Ensemble learning
  • Instance-level constraints

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