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Conference Paper: Mining frequent spatio-temporal sequential patterns

TitleMining frequent spatio-temporal sequential patterns
Authors
Issue Date2005
PublisherIEEE, Computer Society.
Citation
Proceedings - Ieee International Conference On Data Mining, Icdm, 2005, p. 82-89 How to Cite?
AbstractMany applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatio-temporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorithms for transaction data are not directly applicable for this setting. The challenges to address are (i) the fuzziness of locations in patterns, and (ii) the identification of non-explicit pattern instances. In this paper, we define pattern elements as spatial regions around frequent line segments. Our method first transforms the original sequence into a list of sequence segments, and detects frequent regions in a heuristic way. Then, we propose algorithms to find patterns by employing a newly proposed substring tree structure and improving Apriori technique. A performance evaluation demonstrates the effectiveness and efficiency of our approach. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/45546
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorCao, Hen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorCheung, DWen_HK
dc.date.accessioned2007-10-30T06:28:54Z-
dc.date.available2007-10-30T06:28:54Z-
dc.date.issued2005en_HK
dc.identifier.citationProceedings - Ieee International Conference On Data Mining, Icdm, 2005, p. 82-89en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45546-
dc.description.abstractMany applications track the movement of mobile objects, which can be represented as sequences of timestamped locations. Given such a spatio-temporal series, we study the problem of discovering sequential patterns, which are routes frequently followed by the object. Sequential pattern mining algorithms for transaction data are not directly applicable for this setting. The challenges to address are (i) the fuzziness of locations in patterns, and (ii) the identification of non-explicit pattern instances. In this paper, we define pattern elements as spatial regions around frequent line segments. Our method first transforms the original sequence into a list of sequence segments, and detects frequent regions in a heuristic way. Then, we propose algorithms to find patterns by employing a newly proposed substring tree structure and improving Apriori technique. A performance evaluation demonstrates the effectiveness and efficiency of our approach. © 2005 IEEE.en_HK
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dc.format.mimetypeapplication/pdf-
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dc.languageengen_HK
dc.publisherIEEE, Computer Society.en_HK
dc.relation.ispartofProceedings - IEEE International Conference on Data Mining, ICDMen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.titleMining frequent spatio-temporal sequential patternsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1550-4786&volume=&spage=&epage=&date=2005&atitle=Mining+frequent+spatio-temporal+sequential+patternsen_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.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICDM.2005.95en_HK
dc.identifier.scopuseid_2-s2.0-34547303670en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547303670&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage82en_HK
dc.identifier.epage89en_HK
dc.identifier.scopusauthoridCao, H=7403346030en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.citeulike2254608-

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