NDLI: Density-based probabilistic clustering of uncertain moving objects

Content Provider	IEEE Xplore Digital Library
Author	Huajie Xu Xiaoming Hu Bing Yang Juan Xu
Copyright Year	2009
Description	Author affiliation: School of Computer and Information, Shanghai Second Polytechnic, University, China (Huajie Xu; Xiaoming Hu) \|\| School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Bing Yang) \|\| School of Electronics and Information Engineering, Tongji University, Shanghai, China (Juan Xu)
Abstract	In the environment with objects moving randomly, the positions of moving objects can be modeled as a range of possible values, associated with a probability density function. Data mining of such positions of uncertain moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a density-based probabilistic clustering algorithm for uncertain moving objects is proposed, based on DBSCAN algorithm and probabilistic index on uncertain moving objects. Simulation results show that the proposed algorithm outperforms other density-based clustering algorithm for uncertain moving objects in accuracy and update rate needed for clustering.
Starting Page	847
Ending Page	851
File Size	93460
Page Count	5
File Format	PDF
ISBN	9781424447541
DOI	10.1109/ICICISYS.2009.5358040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Shape Moving objects Noise shaping Data mining Clustering algorithm Computer science Monte Carlo methods Databases Clustering algorithms Probability density function Sampling methods Uncertain data
Content Type	Text
Resource Type	Article

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