On combining multiple clusterings: an overview and a new perspective

  • Authors:
  • Tao Li;Mitsunori Ogihara;Sheng Ma

  • Affiliations:
  • School of Computer Science, Florida International University, Miami, USA 33199;Department of Computer Science, University of Miami, Coral Gables, USA 33146;Machine Learning for Systems, IBM T.J. Watson Research Center, Hawthorne, USA 10532

  • Venue:
  • Applied Intelligence
  • Year:
  • 2010

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Abstract

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.