research-article

A hierarchical model-based approach to co-clustering high-dimensional data

Authors:

Giuseppe Manco,

Riccardo OrtaleAuthors Info & Claims

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

Pages 886 - 890

https://doi.org/10.1145/1363686.1363891

Published: 16 March 2008 Publication History

Abstract

We propose a hierarchical, model-based co-clustering framework for handling high-dimensional datasets. The technique views the dataset as a joint probability distribution over row and column variables. Our approach starts by clustering tuples in a dataset, where each cluster is characterized by a different probability distribution. Subsequently, the conditional distribution of attributes over tuples is exploited to discover natural co-clusters in the data. An intensive empirical evaluation highlights the effectiveness of our approach.

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Cited By

Battaglia EPeiretti FPensa R(2024)Co-clustering: A Survey of the Main Methods, Recent Trends, and Open ProblemsACM Computing Surveys10.1145/369887557:2(1-33)Online publication date: 4-Oct-2024
https://dl.acm.org/doi/10.1145/3698875
Costa GOrtale R(2022)Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clustersInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.05.002147(23-39)Online publication date: Aug-2022
https://doi.org/10.1016/j.ijar.2022.05.002
Wu XCheng CZurita-Milla RSong C(2020)An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clusteringInternational Journal of Geographical Information Science10.1080/13658816.2020.172692234:9(1822-1848)Online publication date: 16-Feb-2020
https://doi.org/10.1080/13658816.2020.1726922
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A hierarchical model-based approach to co-clustering high-dimensional data

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cover image ACM Conferences

SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

March 2008

2586 pages

ISBN:9781595937537

DOI:10.1145/1363686

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Hisham M. Haddad
Kennesaw State University

Copyright © 2008 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2008

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SIGAPP

SAC '08: The 2008 ACM Symposium on Applied Computing

March 16 - 20, 2008

Fortaleza, Ceara, Brazil

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Battaglia EPeiretti FPensa R(2024)Co-clustering: A Survey of the Main Methods, Recent Trends, and Open ProblemsACM Computing Surveys10.1145/369887557:2(1-33)Online publication date: 4-Oct-2024
https://dl.acm.org/doi/10.1145/3698875
Costa GOrtale R(2022)Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clustersInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.05.002147(23-39)Online publication date: Aug-2022
https://doi.org/10.1016/j.ijar.2022.05.002
Wu XCheng CZurita-Milla RSong C(2020)An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clusteringInternational Journal of Geographical Information Science10.1080/13658816.2020.172692234:9(1822-1848)Online publication date: 16-Feb-2020
https://doi.org/10.1080/13658816.2020.1726922
Costa GOrtale R(2018)Topical Cluster Discovery in Semistructured Healthcare Data2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.00014(772-775)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.00014
Costa GOrtale R(2016)Structure-Oriented Techniques for XML Document PartitioningNovel Applications of Intelligent Systems10.1007/978-3-319-14194-7_9(167-182)Online publication date: 28-Jan-2016
https://doi.org/10.1007/978-3-319-14194-7_9
Costa GOrtale R(2015)Fully-Automatic XML Clustering by Structure-Constrained PhrasesProceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2015.34(146-153)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/ICTAI.2015.34
Costa GOrtale R(2015)Mining Clusters in XML Corpora Based on Bayesian Generative Topic Modeling2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2015.148(515-520)Online publication date: Dec-2015
https://doi.org/10.1109/ICMLA.2015.148
Costa GOrtale RJia YRokne JWu XEster MXu G(2014)A unified generative bayesian model for community discovery and role assignment based upon latent interaction factorsProceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3191835.3191854(93-100)Online publication date: 17-Aug-2014
https://dl.acm.org/doi/10.5555/3191835.3191854
Costa GOrtale R(2014)XML Document Co-clustering via Non-negative Matrix Tri-factorizationProceedings of the 2014 IEEE 26th International Conference on Tools with Artificial Intelligence10.1109/ICTAI.2014.96(607-614)Online publication date: 10-Nov-2014
https://dl.acm.org/doi/10.1109/ICTAI.2014.96
Costa GOrtale R(2014)A unified generative Bayesian model for community discovery and role assignment based upon latent interaction factors2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)10.1109/ASONAM.2014.6921566(93-100)Online publication date: Aug-2014
https://doi.org/10.1109/ASONAM.2014.6921566
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