Discovering pan-correlation patterns from time course data sets by efficient mining algorithms

Publication Type:
Journal Article
Computing, 2018, 100 (4), pp. 421 - 437
Issue Date:
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© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Time-course correlation patterns can be positive or negative, and time-lagged with gaps. Mining all these correlation patterns help to gain broad insights on variable dependencies. Here, we prove that diverse types of correlation patterns can be represented by a generalized form of positive correlation patterns. We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations. Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (1) the algorithm has linear time increment in terms of increasing number of variables; (2) negative correlation patterns are abundant in real-world data sets; and (3) correlation patterns with time lags and gaps are abundant. Existing methods have only discovered incomplete forms of many of these patterns, and have missed some important patterns completely.
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