A compact representation of spatio-temporal data

Sigal Elnekave, Mark Last, Oded Maimon

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

10 Scopus citations

Abstract

As technology advances we encounter more available data on moving objects, which can be mined to our benefit. In order to efficiently mine this large amount of data we propose an enhanced segmentation algorithm for representing a periodic spatio-temporal trajectory, as a compact set of minimal bounding boxes (MBBs). We also introduce a new, "data-amount-based" similarity measure between mobile trajectories which is compared empirically to an existing similarity measure by clustering spatio-temporal data and evaluating the quality of clusters and the execution times. Finally, we evaluate the values of segmentation thresholds used by the proposed segmentation algorithm through studying the tradeoff between running times and clustering validity as the segmentation resolution increases.

Original languageEnglish
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages601-606
Number of pages6
ISBN (Print)0769530192, 9780769530192
DOIs
StatePublished - 1 Jan 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: 28 Oct 200731 Oct 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Country/TerritoryUnited States
CityOmaha, NE
Period28/10/0731/10/07

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