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Conference Paper: Density-based Place Clustering in Geo-social Networks

TitleDensity-based Place Clustering in Geo-social Networks
Authors
Issue Date2014
PublisherAssociation for Computing Machinery (ACM).
Citation
The Association for Computing Machinery (ACM), Special Interest Group on Management of Data (SIGMOD)/Principles of Database Systems (PODS) Conference, Snowbird, Utah, USA, 22-27 June 2014. In the Proceedings of the ACM SIGMOD International Conference on Management of Data, 2014, p. 99-110 How to Cite?
AbstractSpatial clustering deals with the unsupervised grouping of places into clusters and finds important applications in urban planning and marketing. Current spatial clustering models disregard information about the people who are related to the clustered places. In this paper, we show how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network. Our model considers both spatial information and the social relationships between users who visit the clustered places. After formally defining the model and the distance measure it relies on, we present efficient algorithms for its implementation, based on spatial indexing. We evaluate the effectiveness of our model via a case study on real data; in addition, we design two quantitative measures, called social entropy and community score to evaluate the quality of the discovered clusters. The results show that geo-social clusters have special properties and cannot be found by applying simple spatial clustering approaches. The efficiency of our index-based implementation is also evaluated experimentally.
Persistent Identifierhttp://hdl.handle.net/10722/198600
ISBN

 

DC FieldValueLanguage
dc.contributor.authorShi, Jen_US
dc.contributor.authorMamoulis, Nen_US
dc.contributor.authorWu, Den_US
dc.contributor.authorCheung, DWLen_US
dc.date.accessioned2014-07-07T08:09:38Z-
dc.date.available2014-07-07T08:09:38Z-
dc.date.issued2014en_US
dc.identifier.citationThe Association for Computing Machinery (ACM), Special Interest Group on Management of Data (SIGMOD)/Principles of Database Systems (PODS) Conference, Snowbird, Utah, USA, 22-27 June 2014. In the Proceedings of the ACM SIGMOD International Conference on Management of Data, 2014, p. 99-110en_US
dc.identifier.isbn9781450323765-
dc.identifier.urihttp://hdl.handle.net/10722/198600-
dc.description.abstractSpatial clustering deals with the unsupervised grouping of places into clusters and finds important applications in urban planning and marketing. Current spatial clustering models disregard information about the people who are related to the clustered places. In this paper, we show how the density-based clustering paradigm can be extended to apply on places which are visited by users of a geo-social network. Our model considers both spatial information and the social relationships between users who visit the clustered places. After formally defining the model and the distance measure it relies on, we present efficient algorithms for its implementation, based on spatial indexing. We evaluate the effectiveness of our model via a case study on real data; in addition, we design two quantitative measures, called social entropy and community score to evaluate the quality of the discovered clusters. The results show that geo-social clusters have special properties and cannot be found by applying simple spatial clustering approaches. The efficiency of our index-based implementation is also evaluated experimentally.-
dc.languageengen_US
dc.publisherAssociation for Computing Machinery (ACM).-
dc.relation.ispartofACM SIGMOD/PODS Conferenceen_US
dc.rightsACM SIGMOD/PODS Conference. Copyright © Association for Computing Machinery.-
dc.titleDensity-based Place Clustering in Geo-social Networksen_US
dc.typeConference_Paperen_US
dc.identifier.emailMamoulis, N: nikos@cs.hku.hken_US
dc.identifier.emailWu, D: dmwu@cs.hku.hken_US
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_US
dc.identifier.authorityMamoulis, N=rp00155en_US
dc.identifier.authorityCheung, DWL=rp00101en_US
dc.identifier.doi10.1145/2588555.2610497-
dc.identifier.hkuros230024en_US
dc.identifier.spage99en_US
dc.identifier.epage110en_US
dc.publisher.placeNew York, NY-

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