On combining multiple clusterings

Tao Li, Mitsunori Ogihara, Sheng Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
EditorsD.A. Evans, L. Gravano, O. Herzog, C. Zhai, M. Ronthaler
Pages294-303
Number of pages10
StatePublished - 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

Fingerprint

Clustering
Scenarios
Music
Kinship
Experiment

Keywords

  • Categorical
  • Combining
  • Multiple clusterings

ASJC Scopus subject areas

  • Business, Management and Accounting(all)

Cite this

Li, T., Ogihara, M., & Ma, S. (2004). On combining multiple clusterings. In D. A. Evans, L. Gravano, O. Herzog, C. Zhai, & M. Ronthaler (Eds.), International Conference on Information and Knowledge Management, Proceedings (pp. 294-303)

On combining multiple clusterings. / Li, Tao; Ogihara, Mitsunori; Ma, Sheng.

International Conference on Information and Knowledge Management, Proceedings. ed. / D.A. Evans; L. Gravano; O. Herzog; C. Zhai; M. Ronthaler. 2004. p. 294-303.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, T, Ogihara, M & Ma, S 2004, On combining multiple clusterings. in DA Evans, L Gravano, O Herzog, C Zhai & M Ronthaler (eds), International Conference on Information and Knowledge Management, Proceedings. pp. 294-303, CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, Washington, DC, United States, 11/8/04.
Li T, Ogihara M, Ma S. On combining multiple clusterings. In Evans DA, Gravano L, Herzog O, Zhai C, Ronthaler M, editors, International Conference on Information and Knowledge Management, Proceedings. 2004. p. 294-303
Li, Tao ; Ogihara, Mitsunori ; Ma, Sheng. / On combining multiple clusterings. International Conference on Information and Knowledge Management, Proceedings. editor / D.A. Evans ; L. Gravano ; O. Herzog ; C. Zhai ; M. Ronthaler. 2004. pp. 294-303
@inproceedings{3e12f9817bda44119b8633fdd1109f11,
title = "On combining multiple clusterings",
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.",
keywords = "Categorical, Combining, Multiple clusterings",
author = "Tao Li and Mitsunori Ogihara and Sheng Ma",
year = "2004",
language = "English (US)",
pages = "294--303",
editor = "D.A. Evans and L. Gravano and O. Herzog and C. Zhai and M. Ronthaler",
booktitle = "International Conference on Information and Knowledge Management, Proceedings",

}

TY - GEN

T1 - On combining multiple clusterings

AU - Li, Tao

AU - Ogihara, Mitsunori

AU - Ma, Sheng

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Categorical

KW - Combining

KW - Multiple clusterings

UR - http://www.scopus.com/inward/record.url?scp=18744406039&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=18744406039&partnerID=8YFLogxK

M3 - Conference contribution

SP - 294

EP - 303

BT - International Conference on Information and Knowledge Management, Proceedings

A2 - Evans, D.A.

A2 - Gravano, L.

A2 - Herzog, O.

A2 - Zhai, C.

A2 - Ronthaler, M.

ER -