File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Discovery of collocation episodes in spatiotemporal data

TitleDiscovery of collocation episodes in spatiotemporal data
Authors
Issue Date2007
Citation
Proceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 823-827 How to Cite?
AbstractGiven a collection of trajectories of moving objects with different types (e.g., pumas, deers, vultures, etc.), we introduce the problem of discovering collocation episodes in them (e.g., if a puma is moving near a deer, then a vulture is also going to move close to the same deer with high probability within the next 3 minutes). Collocation episodes catch the inter-movement regularities among different types of objects. We formally define the problem of mining collocation episodes and propose two scaleable algorithms for its efficient solution. We empirically evaluate the performance of the proposed methods using synthetically generated data that emulate real-world object movements. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/93249
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorCao, Hen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorCheung, DWen_HK
dc.date.accessioned2010-09-25T14:55:23Z-
dc.date.available2010-09-25T14:55:23Z-
dc.date.issued2007en_HK
dc.identifier.citationProceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 823-827en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93249-
dc.description.abstractGiven a collection of trajectories of moving objects with different types (e.g., pumas, deers, vultures, etc.), we introduce the problem of discovering collocation episodes in them (e.g., if a puma is moving near a deer, then a vulture is also going to move close to the same deer with high probability within the next 3 minutes). Collocation episodes catch the inter-movement regularities among different types of objects. We formally define the problem of mining collocation episodes and propose two scaleable algorithms for its efficient solution. We empirically evaluate the performance of the proposed methods using synthetically generated data that emulate real-world object movements. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - IEEE International Conference on Data Mining, ICDMen_HK
dc.titleDiscovery of collocation episodes in spatiotemporal dataen_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.doi10.1109/ICDM.2006.59en_HK
dc.identifier.scopuseid_2-s2.0-49749115027en_HK
dc.identifier.hkuros135465en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34748881197&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage823en_HK
dc.identifier.epage827en_HK
dc.identifier.scopusauthoridCao, H=7403346030en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats