Density-based Probabilistic Clustering of Uncertain Moving Objects

被引:0
作者
Xu, Huajie [1 ]
Hu, Xiaoming [1 ]
Yang, Bing [2 ]
Xu, Juan [3 ]
机构
[1] Shanghai Second Polytech Univ, Sch Comp & Informat, Shanghai, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China
[3] Tongji Univ, Sch Elect & Informat Engn, Shanghai 200092, Peoples R China
来源
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年
关键词
clustering algorithm; moving objects; uncertain data; probability density function;
D O I
10.1109/ICICISYS.2009.5358040
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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
引用
收藏
页码:847 / +
页数:3
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