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题名: Mining frequent trajectory pattern based on vague space partition
作者: 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
刊名: Knowledge-Based Systems
ISSN号: 0950-7051
出版日期: 2013
卷号: 50, 页码:100-111
收录类别: SCI ; EI
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记录号: WOS:000323875500007
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence
关键词[WOS]: SEQUENTIAL PATTERNS
研究领域[WOS]: Computer Science
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/12501
Appears in Collections:信息服务与智能控制技术研究室_期刊论文

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