Semi-supervised variable weighting for clustering

Chen, L; Zhang, C

Semi-supervised variable weighting for clustering

Chen, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011, 2011, pp. 862 - 871
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011, 2011, pp. 862 - 871	en_US
dc.identifier.isbn	9780898719925	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19206
dc.description.abstract	Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semi-supervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering structure usually occurs in a subspace defined by a subset of variables. Besides exploiting both labeled and unlabeled data to effectively identify the real importance of variables, our method embeds variable weighting in the process of semi-supervised clustering, rather than calculating variable weights separately, to ensure the computation efficiency. Our experiments carried out on both synthetic and real data demonstrate that semi-supervised variable weighting significantly improves the clustering accuracy of existing semi-supervised k-means without variable weighting, or with unsupervised variable weighting. Copyright © SIAM.	en_US
dc.relation.ispartof	Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011	en_US
dc.title	Semi-supervised variable weighting for clustering	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Mesa, Arizona, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semi-supervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering structure usually occurs in a subspace defined by a subset of variables. Besides exploiting both labeled and unlabeled data to effectively identify the real importance of variables, our method embeds variable weighting in the process of semi-supervised clustering, rather than calculating variable weights separately, to ensure the computation efficiency. Our experiments carried out on both synthetic and real data demonstrate that semi-supervised variable weighting significantly improves the clustering accuracy of existing semi-supervised k-means without variable weighting, or with unsupervised variable weighting. Copyright © SIAM.

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http://hdl.handle.net/10453/19206