Density-based place clustering in geo-social networks

Jieming Shi, Nikos Mamoulis, Dingming Wu, David W. Cheung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

50 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSIGMOD 2014 - Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages99-110
Number of pages12
ISBN (Print)9781450323765
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014 - Snowbird, UT, United States
Duration: 22 Jun 201427 Jun 2014

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014
CountryUnited States
CitySnowbird, UT
Period22/06/1427/06/14

Keywords

  • Density-based clustering
  • Geo-social network
  • Spatial indexing

ASJC Scopus subject areas

  • Software
  • Information Systems

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