Semi-supervised Variable Weighting for Clustering

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Show simple item record Chen, L Zhang, C
dc.contributor.editor Clifton, C
dc.contributor.editor Washio, T 2012-10-12T03:36:21Z 2011-01
dc.identifier.citation Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011, pp. 863 - 871
dc.identifier.isbn 978-0-898719-92-5
dc.identifier.other E1 en_US
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.
dc.format Jessica Robinson
dc.publisher SIAM / Omnipress
dc.title Semi-supervised Variable Weighting for Clustering
dc.type Conference Proceeding
dc.parent Proceedings of the Eleventh SIAM International Conference on Data Mining
dc.journal.number en_US
dc.publocation CA, USA en_US
dc.publocation Berlin
dc.identifier.startpage 863 en_US
dc.identifier.endpage 871 en_US QCIS Investment Core en_US
dc.conference Verified OK en_US
dc.conference SDM
dc.for 080610 Information Systems Organisation
dc.for 080604 Database Management
dc.personcode 011221
dc.personcode 108889
dc.percentage 50 en_US Database Management en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.edition 1st
dc.custom SDM en_US 20110428 en_US 2011-04-28
dc.location.activity Mesa, Arizona, USA en_US
dc.description.keywords variable weighting, semi-supervised clustering, Semi-supervised learning en_US
dc.description.keywords Biochemistry - Molecular biology - Plants - Physiology - Phytoremediation - Selenium - Volatilisation
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)
utslib.collection.history Uncategorised (ID: 363)

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