By Topic

Probabilistic clustering location data of moving objects in mobile computing environment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Huajie Xu ; School of Electronics and Information, Tongji University, Shanghai, China ; Fulin Wang ; Hongya Wang

Data uncertainty is often involved in moving object tracking in mobile computing environment due to reasons such as imprecise measurement or sampling errors. Data mining of such positions of moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a probabilistic clustering algorithm for location data of moving objects is proposed, based on DBSCAN algorithm and probabilistic index on moving objects. Experiment results show that the proposed algorithm outperforms other clustering algorithm we knew for moving objects in update rate needed and efficiency of clustering.

Published in:

Networking and Digital Society (ICNDS), 2010 2nd International Conference on  (Volume:2 )

Date of Conference:

30-31 May 2010