Mining time relaxed gradual moving object clusters

Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire

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

8 Scopus citations

Abstract

One of the objectives of spatio-temporal data mining is to analyze moving object datasets to exploit interesting patterns. Traditionally, existing methods only focus on an unchanged group of moving objects during a time period. Thus, they cannot capture object moving trends which can be very useful for better understanding the natural moving behavior in various real world applications. In this paper, we present a novel concept of "time relaxed gradual trajectory pattern", denoted real-Gpattern, which captures the object movement tendency. Additionally, we also propose an efficient algorithm, called ClusterGrowth, designed to extract the complete set of all interesting maximal real-Gpatterns. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.

Original languageEnglish (US)
Title of host publication20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
Pages478-481
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012 - Redondo Beach, CA, United States
Duration: Nov 6 2012Nov 9 2012

Publication series

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

Other

Other20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2012
CountryUnited States
CityRedondo Beach, CA
Period11/6/1211/9/12

All Science Journal Classification (ASJC) codes

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

Keywords

  • gradual moving object cluster
  • gradual trajectories

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