OPTICS与离线批处理在轨迹聚类中的应用  

Application of OPTICS and Offline Batch Processing in Trajectory Clustering

作　　者：郭雨 陈金勇[2] 张新宇 李梁 孙未未[1] GUO Yu;CHEN Jinyong;ZHANG Xinyu;LI Liang;SUN Weiwei(School of Computer Science,Fudan University,Shanghai 201203,China;CETC Key Laboratory of Aerospace Information Applications,Shijiazhuang 050081,China)

机构地区：[1]复旦大学计算机科学技术学院,上海201203 [2]中国电子科技集团公司航天信息应用技术重点实验室,石家庄050081

出　　处：《计算机工程》2020年第7期72-77,83,共7页Computer Engineering

基　　金：国家自然科学基金面上项目(61772138);中国电子科技集团公司航天信息应用技术重点实验室开放基金。

摘　　要：轨迹聚类是时空轨迹处理中的重要步骤,常用的轨迹聚类算法如TRACLUS算法,时间复杂度通常较高且对输入参数敏感,在寻找最优参数的过程中会消耗大量的时间。针对该问题,对TRACLUS算法运用离线批处理技术与OPTICS算法进行改进,在缓解输入参数敏感性的同时,减少对多组参数进行轨迹聚类的时间,从而减轻人为参数调试的工作量。实验结果表明,在最优参数未知需要对多组参数进行测试时,改进算法可使运行效率得到大幅提升。Trajectory clustering is an important step in spatio-temporal trajectory processing.Common trajectory clustering algorithms,such as TRACLUS algorithm,usually have high time complexity and are sensitive to input parameters,thus consuming a lot of time to find optimal parameters.In order to solve this problem,this paper improves the TRACLUS algorithm by using offline batch processing technology and OPTICS algorithm.This optimization reduces the sensitivity of input parameters and the time for trajectory clustering of multiple sets of parameters,so the workload of the manual parameter debugging is reduced.Experimental results show that the time efficiency of the algorithm has been greatly improved when the optimal parameters are unknown and multiple sets of parameters need to be tested.

关 键 词：时空轨迹聚类 密度聚类 OPTICS算法 离线批处理 TRACLUS算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]