On combining multiple clusterings

Tao Li, Mitsunori Ogihara, Sheng Ma

Research output: Contribution to conferencePaper

15 Scopus citations

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.

Original languageEnglish (US)
Pages294-303
Number of pages10
StatePublished - Dec 1 2004
Externally publishedYes
EventCIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management - Washington, DC, United States
Duration: Nov 8 2004Nov 13 2004

Other

OtherCIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
CountryUnited States
CityWashington, DC
Period11/8/0411/13/04

Keywords

  • Categorical
  • Combining
  • Multiple clusterings

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Li, T., Ogihara, M., & Ma, S. (2004). On combining multiple clusterings. 294-303. Paper presented at CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, Washington, DC, United States.