An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

作     者：Yujie Zhang Genlin Ji Bin Zhao Bo Sheng 

作者机构：School of Computer Science and TechnologyNanjing Normal UniversityNanjing210023China Department of Computer ScienceUniversity of Massachusetts Boston100 Morrissey BoulevardBostonMAUSA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第59卷第6期

页      面：885-901页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by the National Natural Science Foundationof China under Grants No. 41471371 

主　　题：Trajectory streams pattern mining moving object clusters pattern discovery of moving clusters pattern 

摘      要：The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and *** the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory *** paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window *** processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters ***,the density peaks clustering algorithm is exploited to identify clusters of different *** stable relationship between relatively few moving objects is used to improve the clustering ***,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is *** relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating ***,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.