Abstract
We study the problem of mining all associated itemsets whose prevalence variations are similar to a given reference sequence from temporal databases. The discovered temporal association patterns can reveal interesting relationships of itemsets which co-occur with a particular event over time. A user-defined subset specification which consists of a reference sequence, a similarity function, and a dissimiliarty threshold is used for defining interesting temporal patterns and guiding the similarity search. We develop algorithms with exploring interesting properties for efficiently finding the similar temporal association patterns. Experimental results show that the proposed algorithms are efficient than a naive approach.
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Yoo, J.S., Shekhar, S. (2008). Mining Temporal Association Patterns under a Similarity Constraint. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_26
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DOI: https://doi.org/10.1007/978-3-540-69497-7_26
Publisher Name: Springer, Berlin, Heidelberg
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