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On combining multiple clusterings: an overview and a new perspective

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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.

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Authors and Affiliations

  1. School of Computer Science, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, USA

    Tao Li

  2. Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33146, USA

    Mitsunori Ogihara

  3. Machine Learning for Systems, IBM T.J. Watson Research Center, 17 Skyline Drive, Hawthorne, NY, 10532, USA

    Sheng Ma

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  1. Tao Li
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  2. Mitsunori Ogihara
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  3. Sheng Ma
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Correspondence to Tao Li.

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Li, T., Ogihara, M. & Ma, S. On combining multiple clusterings: an overview and a new perspective. Appl Intell 33, 207–219 (2010). https://doi.org/10.1007/s10489-009-0160-4

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