Skip to Main content Skip to Navigation
Conference papers

Mining Time Relaxed Gradual Moving Object Clusters

Abstract : One of the objectives of spatio-temporal data mining is to analyze moving object datasets to exploit interesting pat- terns. 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [9 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00798076
Contributor : Pascal Poncelet Connect in order to contact the contributor
Submitted on : Thursday, March 21, 2019 - 8:09:41 PM
Last modification on : Friday, September 17, 2021 - 3:38:44 AM
Long-term archiving on: : Saturday, June 22, 2019 - 4:13:55 PM

File

GIS2012.pdf
Files produced by the author(s)

Identifiers

Citation

Nhat Hai Phan, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire. Mining Time Relaxed Gradual Moving Object Clusters. 20th ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL), Nov 2012, Redondo Beach, United States. pp.478-481, ⟨10.1145/2424321.2424394⟩. ⟨lirmm-00798076⟩

Share

Metrics

Record views

257

Files downloads

201