On combining multiple clusterings: An overview and a new perspective

Tao Li, Mitsunori Ogihara, Sheng Ma

Research output: Contribution to journalReview articlepeer-review

25 Scopus citations


Many problems can be reduced to the problem of combining multiple clusterings. In this paper, we first summarize different application scenarios of combining multiple clusterings and provide a new perspective of viewing the problem as a categorical clustering problem. We then show the connections between various consensus and clustering criteria and discuss the complexity results of the problem. Finally we propose a new method to determine the final clustering. Experiments on kinship terms and clustering popular music from heterogeneous feature sets show the effectiveness of combining multiple clusterings.

Original languageEnglish (US)
Pages (from-to)207-219
Number of pages13
JournalApplied Intelligence
Issue number2
StatePublished - Oct 2010


  • Categorical
  • Combining
  • Multiple clusterings

ASJC Scopus subject areas

  • Artificial Intelligence


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