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Mining frequent trajectory pattern based on vague space partition
Wang L(王亮); Hu KY(胡琨元); Ku T(库涛); Yan XH(晏晓辉)
作者部门信息服务与智能控制技术研究室
关键词Algorithms Data Mining
发表期刊Knowledge-Based Systems
ISSN0950-7051
2013
卷号50页码:100-111
收录类别SCI ; EI
EI收录号20133316615301
WOS记录号WOS:000323875500007
产权排序1
摘要Frequent trajectory pattern mining is an important spatiotemporal data mining problem with broad applications. However, it is also a difficult problem due to the approximate nature of spatial trajectory locations. Most of the previously developed frequent trajectory pattern mining methods explore a crisp space partition approach [8,10] to alleviate the spatial approximation concern. However, this approach may cause the sharp boundary problem that spatially close trajectory locations may fall into different partitioned regions, and eventually result in failure of finding meaningful trajectory patterns. In this paper, we propose a flexible vague space partition approach to solve the sharp boundary problem. In this approach, the spatial plane is divided into a set of vague grid cells, and trajectory locations are transformed into neighboring vague grid cells by a distance-based membership function. Based on two classical sequential mining algorithms, the PrefixSpan and GSP algorithms, we propose two efficient trajectory pattern mining algorithms, called VTPM-PrefixSpan and VTPM-GSP, to mine the transformed trajectory sequences with time interval constraints. A comprehensive performance study on both synthetic and real datasets shows that the VTPM-PrefixSpan algorithm outperforms the VTPM-GSP algorithm in both effectiveness and scalability.

语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence
关键词[WOS]SEQUENTIAL PATTERNS
WOS研究方向Computer Science
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/12501
专题信息服务与智能控制技术研究室
通讯作者Wang L(王亮)
作者单位1.Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Graduate School of the Chinese Academy of Sciences, Beijing 100039, China
推荐引用方式
GB/T 7714
Wang L,Hu KY,Ku T,et al. Mining frequent trajectory pattern based on vague space partition[J]. Knowledge-Based Systems,2013,50:100-111.
APA Wang L,Hu KY,Ku T,&Yan XH.(2013).Mining frequent trajectory pattern based on vague space partition.Knowledge-Based Systems,50,100-111.
MLA Wang L,et al."Mining frequent trajectory pattern based on vague space partition".Knowledge-Based Systems 50(2013):100-111.
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