Predicting future locations using clusters' centroids

Sigal Elnekave, Mark Last, Oded Maimon

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

7 Scopus citations

Abstract

As technology advances we encounter more available data on moving objects, thus increasing our ability to mine spatio-temporal data. We can use this data for learning moving objects behavior and for predicting their locations at future times according to the extracted movement patterns. In this paper we cluster trajectories of a mobile object and utilize the accepted cluster centroids as the object's movement patterns. We use the obtained movement patterns for predicting the object location at specific future times. We evaluate our prediction results using precision and recall measures. We also remove exceptional data points from the moving patterns by optimizing the value of an exceptions threshold.

Original languageEnglish
Title of host publicationProceedings of the 15th ACM International Symposium on Advances in Geographic Information Systems, GIS 2007
Pages368-371
Number of pages4
DOIs
StatePublished - 1 Dec 2007
Event15th ACM International Symposium on Advances in Geographic Information Systems, GIS 2007 - Seattle, WA, United States
Duration: 7 Nov 20079 Nov 2007

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference15th ACM International Symposium on Advances in Geographic Information Systems, GIS 2007
Country/TerritoryUnited States
CitySeattle, WA
Period7/11/079/11/07

Keywords

  • clustering
  • moving objects
  • prediction
  • spatio-temporal data mining

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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