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Conference Paper: Mining frequent spatio-temporal sequential patterns
Title | Mining frequent spatio-temporal sequential patterns |
---|---|
Authors | |
Issue Date | 2005 |
Publisher | IEEE, Computer Society. |
Citation | Proceedings - Ieee International Conference On Data Mining, Icdm, 2005, p. 82-89 How to Cite? |
Abstract | Many 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 Identifier | http://hdl.handle.net/10722/45546 |
ISSN | 2020 SCImago Journal Rankings: 0.545 |
References |
DC Field | Value | Language |
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dc.contributor.author | Cao, H | en_HK |
dc.contributor.author | Mamoulis, N | en_HK |
dc.contributor.author | Cheung, DW | en_HK |
dc.date.accessioned | 2007-10-30T06:28:54Z | - |
dc.date.available | 2007-10-30T06:28:54Z | - |
dc.date.issued | 2005 | en_HK |
dc.identifier.citation | Proceedings - Ieee International Conference On Data Mining, Icdm, 2005, p. 82-89 | en_HK |
dc.identifier.issn | 1550-4786 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/45546 | - |
dc.description.abstract | Many 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 |
dc.format.extent | 477383 bytes | - |
dc.format.extent | 4295 bytes | - |
dc.format.extent | 6619 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | text/plain | - |
dc.format.mimetype | text/plain | - |
dc.language | eng | en_HK |
dc.publisher | IEEE, Computer Society. | en_HK |
dc.relation.ispartof | Proceedings - IEEE International Conference on Data Mining, ICDM | en_HK |
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. | - |
dc.title | Mining frequent spatio-temporal sequential patterns | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1550-4786&volume=&spage=&epage=&date=2005&atitle=Mining+frequent+spatio-temporal+sequential+patterns | en_HK |
dc.identifier.email | Mamoulis, N:nikos@cs.hku.hk | en_HK |
dc.identifier.email | Cheung, DW:dcheung@cs.hku.hk | en_HK |
dc.identifier.authority | Mamoulis, N=rp00155 | en_HK |
dc.identifier.authority | Cheung, DW=rp00101 | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.doi | 10.1109/ICDM.2005.95 | en_HK |
dc.identifier.scopus | eid_2-s2.0-34547303670 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-34547303670&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 82 | en_HK |
dc.identifier.epage | 89 | en_HK |
dc.identifier.scopusauthorid | Cao, H=7403346030 | en_HK |
dc.identifier.scopusauthorid | Mamoulis, N=6701782749 | en_HK |
dc.identifier.scopusauthorid | Cheung, DW=34567902600 | en_HK |
dc.identifier.citeulike | 2254608 | - |
dc.identifier.issnl | 1550-4786 | - |