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.
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Wang, J., Hsu, W., Lee, M.L. (2005). Mining Generalized Spatio-Temporal Patterns. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_60
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DOI: https://doi.org/10.1007/11408079_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25334-1
Online ISBN: 978-3-540-32005-0
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