Towards mobility-based clustering

Siyuan Liu, Yunhuai Liu, Lionel M. Ni, Jianping Fan, Minglu Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

139 Scopus citations

Abstract

Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The practical research on hot spots in smart city presents many unique features, such as highly mobile environments, supremely limited size of sample objects, and the non-uniform, biased samples. All these features have raised new challenges that make the traditional density-based clustering algorithms fail to capture the real clustering property of objects, making the results less meaningful. In this paper we propose a novel, non-density-based approach called mobility-based clustering. The key idea is that sample objects are employed as "sensors" to perceive the vehicle crowdedness in nearby areas using their instant mobility, rather than the "object representatives". As such the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified and techniques to compensate these effects are proposed. We evaluate the performance of mobility-based clustering based on real traffic situations. Experimental results show that using 0.3 % of vehicles as the samples, mobility-based clustering can accurately identify hot spots which can hardly be obtained by the latest representative algorithm UMicro.

Original languageEnglish (US)
Title of host publicationKDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
Pages919-927
Number of pages9
DOIs
StatePublished - Sep 7 2010
Event16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010 - Washington, DC, United States
Duration: Jul 25 2010Jul 28 2010

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010
CountryUnited States
CityWashington, DC
Period7/25/107/28/10

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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