Mining generalized spatio-temporal patterns

被引：0

作者：

Wang, JM ^{[1
]}

Hsu, WN ^{[1
]}

Lee, ML ^{[1
]}

机构：

[1] Natl Univ Singapore, Sch Comp, Singapore 117543, Singapore

来源：

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PROCEEDINGS | 2005年 / 3453卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Spatio-temporal databases offer a rich repository and opportunities to develop techniques for discovering new types of spatio-temporal patterns. In this paper, we introduce a new class of spatio-temporal patterns, called the generalized spatio-temporal patterns, to describe the repeated sequences of events that occur within small neighbourhoods. Such patterns are crucial to the understanding of habitual patterns. To discover this class of patterns, we develop an algorithm GenSTMiner based on the idea of pattern growth approach, and introduce some optimization techniques that are used to reduce the number of candidates generated and minimize the size of the projected databases. Our performance study indicates that GenSTMiner is highly efficient and outperforms PrefixSpan.

引用

页码：649 / 661

页数：13