Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40590
Title: Mining generalized spatio-temporal patterns
Authors: Wang, J. 
Hsu, W. 
Lee, M.L. 
Issue Date: 2005
Source: Wang, J.,Hsu, W.,Lee, M.L. (2005). Mining generalized spatio-temporal patterns. Lecture Notes in Computer Science 3453 : 649-661. ScholarBank@NUS Repository.
Abstract: 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. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science
URI: http://scholarbank.nus.edu.sg/handle/10635/40590
ISSN: 03029743
Appears in Collections:Staff Publications

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