Title

Similarity-Profiled Temporal Association Mining

Document Type

Article

Publication Date

8-2009

Publication Source

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

21

Issue

5

Inclusive pages

1147-1161

DOI

10.1109/TKDE.2008.185

Publisher

Elsevier B.V.,

Place of Publication

United States

ISBN/ISSN

10414347

Abstract

Given a time stamped transaction database and a user-defined reference sequence of interest overtime, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.

Disciplines

Computer Sciences

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