Article

On combining multiple clusterings

Authors:

Mitsunori Ogihara,

Sheng MaAuthors Info & Claims

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

Pages 294 - 303

https://doi.org/10.1145/1031171.1031234

Published: 13 November 2004 Publication History

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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Cited By

Zamora JSublime J(2023)An Ensemble and Multi-View Clustering Method Based on Kolmogorov ComplexityEntropy10.3390/e2502037125:2(371)Online publication date: 17-Feb-2023
https://doi.org/10.3390/e25020371
Li HYe XImakura ASakurai T(2023)LSEC: Large-scale spectral ensemble clusteringIntelligent Data Analysis10.3233/IDA-21624027:1(59-77)Online publication date: 30-Jan-2023
https://doi.org/10.3233/IDA-216240
Koutrouli MKaratzas EPaez-Espino DPavlopoulos G(2020)A Guide to Conquer the Biological Network Era Using Graph TheoryFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2020.000348Online publication date: 31-Jan-2020
https://doi.org/10.3389/fbioe.2020.00034
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Index Terms

On combining multiple clusterings
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Conferences

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

November 2004

678 pages

ISBN:1581138741

DOI:10.1145/1031171

General Chair:
David Grossman
Illinois Institute of Technology
,
Program Chairs:
Luis Gravano
Columbia University
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign
,
Otthein Herzog
University of Bremen, Germany
,
David A. Evans
Clairvoyance Corporation

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 November 2004

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Cited By

Zamora JSublime J(2023)An Ensemble and Multi-View Clustering Method Based on Kolmogorov ComplexityEntropy10.3390/e2502037125:2(371)Online publication date: 17-Feb-2023
https://doi.org/10.3390/e25020371
Li HYe XImakura ASakurai T(2023)LSEC: Large-scale spectral ensemble clusteringIntelligent Data Analysis10.3233/IDA-21624027:1(59-77)Online publication date: 30-Jan-2023
https://doi.org/10.3233/IDA-216240
Koutrouli MKaratzas EPaez-Espino DPavlopoulos G(2020)A Guide to Conquer the Biological Network Era Using Graph TheoryFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2020.000348Online publication date: 31-Jan-2020
https://doi.org/10.3389/fbioe.2020.00034
Zamora JSublime J(2020)A New Information Theory Based Clustering Fusion Method for Multi-view Representations of Text DocumentsSocial Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis10.1007/978-3-030-49570-1_11(156-167)Online publication date: 10-Jul-2020
https://doi.org/10.1007/978-3-030-49570-1_11
Drobne SLakner M(2018)The Influence of the Zonation Effect on a System of Hierarchical Functional RegionsBusiness Systems Research Journal10.2478/bsrj-2018-00189:2(45-54)Online publication date: 28-Jul-2018
https://doi.org/10.2478/bsrj-2018-0018
Farseev ASamborskii IFilchenkov AChua TKando NSakai TJoho HLi Hde Vries AWhite R(2017)Cross-Domain Recommendation via Clustering on Multi-Layer GraphsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080774(195-204)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080774
Liu HLiu TWu JTao DFu YCao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Spectral Ensemble ClusteringProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783287(715-724)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2783287
Zheng LLi TDing C(2014)A Framework for Hierarchical Ensemble ClusteringACM Transactions on Knowledge Discovery from Data10.1145/26113809:2(1-23)Online publication date: 23-Sep-2014
https://dl.acm.org/doi/10.1145/2611380
Nanda APujari A(2011)Weighted Co-clustering Based Clustering EnsembleProceedings of the 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics10.1109/NCVPRIPG.2011.17(46-49)Online publication date: 15-Dec-2011
https://dl.acm.org/doi/10.1109/NCVPRIPG.2011.17
Li JSwift SLiu X(2010)The effect of cooling functions on ensemble clustering using simulated annealingIntelligent Data Analysis10.5555/1890496.189050314:6(701-730)Online publication date: 15-Nov-2010
https://dl.acm.org/doi/10.5555/1890496.1890503
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