Challenges and Issues in Trajectory Streams Clustering upon a Sliding-window Model

摘要：

　　The proliferation of location-acquisition devices and thriving development of social websites enable analyzing usersmovement behaviors and detecting social events in dynamic trajectory streams.In this paper, we firstly analyze the challenges in trajectory stream clustering, and then depict a three-part framework to deal with this issue, that includes i)trajectory data pre-processing for higher quality, ii) online micro-clustering to summarize a large number of microclusters, and iii) offline macro-clustering to form the resulting clusters.Particularly, we present the in-cluster maintenance strategy for online clustering evolving trajectory streams over sliding windows.It can eliminate the obsolete data while adaptively maintaining the summary statistics for continuously arriving location data, and thus avoid performance degradation with minimal harm to result quality.

关键词： clustering trajectory stream sliding window

作者: Jiali Mao Cheqing Jin Xiaoling Wang Aoying Zhou

作者单位: Institute for Data Science and Engineering, Software Engineering Institute East China Normal University Shanghai, China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 303-308

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）