Semi-supervised Variable Weighting for Clustering

UTSePress Research/Manakin Repository

Search UTSePress Research


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Chen, Ling en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.editor Chris Clifton, Takashi Washio en_US
dc.date.accessioned 2012-10-12T03:36:21Z
dc.date.available 2012-10-12T03:36:21Z
dc.date.issued 2011 en_US
dc.identifier 2010005231 en_US
dc.identifier.citation Chen Ling and Zhang Chengqi 2011, 'Semi-supervised Variable Weighting for Clustering', , SIAM / Omnipress, CA, USA, , pp. 863-871. en_US
dc.identifier.issn 978-0-898719-92-5 en_US
dc.identifier.other E1 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 signicantly improves the clustering accuracy of existing semi-supervised k-means without variable weighting, or with unsupervised variable weighting. en_US
dc.language en_US
dc.publisher SIAM / Omnipress en_US
dc.relation.isbasedon en_US
dc.title Semi-supervised Variable Weighting for Clustering en_US
dc.parent Proceedings of the Eleventh SIAM International Conference on Data Mining en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation CA, USA en_US
dc.identifier.startpage 863 en_US
dc.identifier.endpage 871 en_US
dc.cauo.name QCIS Investment Core en_US
dc.conference Verified OK en_US
dc.for 080604 en_US
dc.personcode 108889 en_US
dc.personcode 011221 en_US
dc.percentage 50 en_US
dc.classification.name Database Management en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom SDM en_US
dc.date.activity 20110428 en_US
dc.location.activity Mesa, Arizona, USA en_US
dc.description.keywords variable weighting, semi-supervised clustering, Semi-supervised learning en_US
dc.staffid en_US
dc.staffid 011221 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record