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Conference Paper: Clustering objects on a spatial network

TitleClustering objects on a spatial network
Authors
Issue Date2004
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmod
Citation
Proceedings Of The Acm Sigmod International Conference On Management Of Data, 2004, p. 443-454 How to Cite?
AbstractClustering is one of the most important analysis tasks in spatial databases. We study the problem of clustering objects, which lie on edges of a large weighted spatial network. The distance between two objects is defined by their shortest path distance over the network. Past algorithms are based on the Euclidean distance and cannot be applied for this setting. We propose variants of partitioning, density-based, and hierarchical methods. Their effectiveness and efficiency is evaluated for collections of objects which appear on real road networks. The results show that our methods can correctly identify clusters and they are scalable for large problems.
Persistent Identifierhttp://hdl.handle.net/10722/93306
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYiu, MLen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2010-09-25T14:57:07Z-
dc.date.available2010-09-25T14:57:07Z-
dc.date.issued2004en_HK
dc.identifier.citationProceedings Of The Acm Sigmod International Conference On Management Of Data, 2004, p. 443-454en_HK
dc.identifier.issn0730-8078en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93306-
dc.description.abstractClustering is one of the most important analysis tasks in spatial databases. We study the problem of clustering objects, which lie on edges of a large weighted spatial network. The distance between two objects is defined by their shortest path distance over the network. Past algorithms are based on the Euclidean distance and cannot be applied for this setting. We propose variants of partitioning, density-based, and hierarchical methods. Their effectiveness and efficiency is evaluated for collections of objects which appear on real road networks. The results show that our methods can correctly identify clusters and they are scalable for large problems.en_HK
dc.languageengen_HK
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmoden_HK
dc.relation.ispartofProceedings of the ACM SIGMOD International Conference on Management of Dataen_HK
dc.titleClustering objects on a spatial networken_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-3142660421en_HK
dc.identifier.hkuros103379en_HK
dc.identifier.spage443en_HK
dc.identifier.epage454en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK

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