Incremental clustering of mobile objects

Sigal Elnekave, Mark Last, Oded Maimon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations


Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication devices. Mining spatio-temporal data can benefit many different functions: marketing team managers for identifying the right customers at the right time, cellular companies for optimizing the resources allocation, web site administrators for data allocation matters, animal migration researchers for understanding migration patterns, and meteorology experts for weather forecasting. In this research we use a compact representation of a mobile trajectory and define a new similarity measure between trajectories. We also propose an incremental clustering algorithm for finding evolving groups of similar mobile objects in spatio-temporal data. The algorithm is evaluated empirically by the quality of object clusters (using Dunn and Rand indexes), memory space efficiency, execution times, and scalability (run time vs. number of objects).

Original languageEnglish
Title of host publicationWorkshops in Conjunction with the International Conference on Data Engineering - ICDE' 07
PublisherIEEE Computer Society
Number of pages8
ISBN (Print)1424408326, 9781424408320
StatePublished - 1 Jan 2007
EventWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 - Istanbul, Turkey
Duration: 15 Apr 200720 Apr 2007

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


ConferenceWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007


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