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Conference Paper: On discovering moving clusters in spatio-temporal data

TitleOn discovering moving clusters in spatio-temporal data
Authors
Issue Date2005
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science, 2005, v. 3633, p. 364-381 How to Cite?
AbstractA moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The difference of this problem compared to clustering trajectories and mining movement patterns is that the identity of a moving cluster remains unchanged while its location and content may change over time. For example, while a group of animals are migrating, some animals may leave the group or new animals may enter it. We provide a formal definition for moving clusters and describe three algorithms for their automatic discovery: (i) a straight-forward method based on the definition, (ii) a more efficient method which avoids redundant checks and (iii) an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding. The experimental results demonstrate the efficiency of our techniques and their applicability to large spatio-temporal datasets. © Springer-Verlag Berlin Heidelberg 2005.
Persistent Identifierhttp://hdl.handle.net/10722/93305
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorKalnis, Pen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorBakiras, Sen_HK
dc.date.accessioned2010-09-25T14:57:05Z-
dc.date.available2010-09-25T14:57:05Z-
dc.date.issued2005en_HK
dc.identifier.citationLecture Notes In Computer Science, 2005, v. 3633, p. 364-381en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93305-
dc.description.abstractA moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The difference of this problem compared to clustering trajectories and mining movement patterns is that the identity of a moving cluster remains unchanged while its location and content may change over time. For example, while a group of animals are migrating, some animals may leave the group or new animals may enter it. We provide a formal definition for moving clusters and describe three algorithms for their automatic discovery: (i) a straight-forward method based on the definition, (ii) a more efficient method which avoids redundant checks and (iii) an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding. The experimental results demonstrate the efficiency of our techniques and their applicability to large spatio-temporal datasets. © Springer-Verlag Berlin Heidelberg 2005.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Scienceen_HK
dc.titleOn discovering moving clusters in spatio-temporal dataen_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-s2.0-26444541854en_HK
dc.identifier.hkuros103352en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-26444541854&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3633en_HK
dc.identifier.spage364en_HK
dc.identifier.epage381en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridKalnis, P=6603477534en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridBakiras, S=9632625700en_HK

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