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dc.contributor.authorLo, Chia-Haoen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.date.accessioned2014-12-08T15:48:56Z-
dc.date.available2014-12-08T15:48:56Z-
dc.date.issued2008en_US
dc.identifier.isbn978-3-540-68124-3en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/32542-
dc.description.abstractPrior works have elaborated on the problem of joint clustering in the optimization and geography domains. However, prior works neither clearly specify the connected constraint in the geography domain nor propose efficient algorithms. In this paper, we formulate the joint clustering problem in which a connected constraint and the number of clusters should be specified. We propose an algorithm K-means with Local Search (abbreviated as KLS) to solve the joint clustering problem with the connected constraint. Experimental results show that KLS can find correct clusters efficiently.en_US
dc.language.isoen_USen_US
dc.titleEfficient joint clustering algorithms in optimization and geography domainsen_US
dc.typeProceedings Paperen_US
dc.identifier.journalADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGSen_US
dc.citation.volume5012en_US
dc.citation.spage945en_US
dc.citation.epage950en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000256127100096-
Appears in Collections:Conferences Paper