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Conference Paper: Fast mining of spatial collocations

TitleFast mining of spatial collocations
Authors
Zhang, XMamoulis, NCheung, DWShou, Y
KeywordsCollocation Pattern
Spatial Databases
Issue Date2004
Citation
Kdd-2004 - Proceedings Of The Tenth Acm Sigkdd International Conference On Knowledge Discovery And Data Mining, 2004, p. 384-393 How to Cite?
AbstractSpatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.
Persistent Identifierhttp://hdl.handle.net/10722/93154
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorZhang, Xen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorShou, Yen_HK
dc.date.accessioned2010-09-25T14:52:31Z-
dc.date.available2010-09-25T14:52:31Z-
dc.date.issued2004en_HK
dc.identifier.citationKdd-2004 - Proceedings Of The Tenth Acm Sigkdd International Conference On Knowledge Discovery And Data Mining, 2004, p. 384-393en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93154-
dc.description.abstractSpatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.en_HK
dc.languageengen_HK
dc.relation.ispartofKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Miningen_HK
dc.subjectCollocation Patternen_HK
dc.subjectSpatial Databasesen_HK
dc.titleFast mining of spatial collocationsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-12244312165en_HK
dc.identifier.hkuros103248en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-12244312165&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage384en_HK
dc.identifier.epage393en_HK
dc.identifier.scopusauthoridZhang, X=7410271827en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridShou, Y=8277999000en_HK

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