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- Publisher Website: 10.1145/2588555.2610497
- Scopus: eid_2-s2.0-84904346258
- WOS: WOS:000454714600010
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Conference Paper: Density-based Place Clustering in Geo-social Networks
Title | Density-based Place Clustering in Geo-social Networks |
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Authors | |
Keywords | Density-based clustering Geo-social network Spatial indexing |
Issue Date | 2014 |
Publisher | Association 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? |
Abstract | Spatial 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 Identifier | http://hdl.handle.net/10722/198600 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Shi, J | en_US |
dc.contributor.author | Mamoulis, N | en_US |
dc.contributor.author | Wu, D | en_US |
dc.contributor.author | Cheung, DWL | en_US |
dc.date.accessioned | 2014-07-07T08:09:38Z | - |
dc.date.available | 2014-07-07T08:09:38Z | - |
dc.date.issued | 2014 | en_US |
dc.identifier.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 | en_US |
dc.identifier.isbn | 9781450323765 | - |
dc.identifier.uri | http://hdl.handle.net/10722/198600 | - |
dc.description.abstract | Spatial 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.language | eng | en_US |
dc.publisher | Association for Computing Machinery (ACM). | - |
dc.relation.ispartof | ACM SIGMOD/PODS Conference | en_US |
dc.rights | ACM SIGMOD/PODS Conference. Copyright © Association for Computing Machinery. | - |
dc.subject | Density-based clustering | - |
dc.subject | Geo-social network | - |
dc.subject | Spatial indexing | - |
dc.title | Density-based Place Clustering in Geo-social Networks | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Mamoulis, N: nikos@cs.hku.hk | en_US |
dc.identifier.email | Wu, D: dmwu@cs.hku.hk | en_US |
dc.identifier.email | Cheung, DWL: dcheung@cs.hku.hk | en_US |
dc.identifier.authority | Mamoulis, N=rp00155 | en_US |
dc.identifier.authority | Cheung, DWL=rp00101 | en_US |
dc.identifier.doi | 10.1145/2588555.2610497 | - |
dc.identifier.scopus | eid_2-s2.0-84904346258 | - |
dc.identifier.hkuros | 230024 | en_US |
dc.identifier.spage | 99 | en_US |
dc.identifier.epage | 110 | en_US |
dc.identifier.isi | WOS:000454714600010 | - |
dc.publisher.place | New York, NY | - |