Mark Last

Senior Academic

Discovering regular groups of mobile objects using incremental clustering

Sigal Elnekave, Mark Last, Oded Maimon, Yehuda Ben-Shimol, Hans Einsiedler, Menahem Friedman, Matthias Siebert

As technology advances, detailed data on the position of moving objects, such as humans and vehicles is available. In order to discover groups of mobile objects that usually move in similar ways we propose an incremental clustering algorithm that clusters mobile objects according to similarity of their movement patterns. The proposed clustering algorithm uses a new, "data-amount- based" similarity measure between mobile trajectories. The clustering algorithm is evaluated on two spatio-temporal datasets using clustering validity measures.

Publication language English
Pages 197-205
Publication status Published - 15.09.2008
Article Number 4510375

Keywords

Clustering
Mobile objects
Spatio-temporal data mining

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Communication
Access to Document
10.1109/WPNC.2008.4510375
Other files and links
Link to publication in Scopus