Improved insensitive to input parameters trajectory clustering algorithm

查看全文 作　　者：Jiashun [1,2]Chen;Dechang [1]Pi

机构地区：[1]College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics;[2]College of Computer Science and Technology,Huaihai Institute of Technology

出　　处：《Journal of Systems Engineering and Electronics》索引2013年第24卷第5期,共10页

基　　金：supported by the National High Technology Research and Development Program of China(863 Program)(2007AA01Z404);the Funding of Jiangsu Provincial Innovation Program for Graduate Education(CXLX110206) 摘　　要：The existing trajectory clustering(TRACLUS) is sensitive to the input parameters ε and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster(SPSTC) algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core distance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster. 关键词：参数敏感性聚类算法输入参数参数灵敏度距离和参数化数值变化到达曲线

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Improved insensitive to input parameters trajectory clustering algorithm

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