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

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Abstract

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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Authors and Affiliations

  1. Advanced Analytics Institute, University of Technology Sydney, Broadway, NSW, 2007, Australia

    Qian Liu, Shameek Ghosh & Jinyan Li

  2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore, Singapore

    Limsoon Wong

  3. Department of Computing and Information Systems, The University of Melbourne, Parkville, VIC, 3010, Australia

    Kotagiri Ramamohanarao

Authors
  1. Qian Liu
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  2. Shameek Ghosh
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  3. Jinyan Li
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  4. Limsoon Wong
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  5. Kotagiri Ramamohanarao
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Correspondence to Jinyan Li.

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Liu, Q., Ghosh, S., Li, J. et al. Discovering pan-correlation patterns from time course data sets by efficient mining algorithms. Computing 100, 421–437 (2018). https://doi.org/10.1007/s00607-018-0606-9

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  • DOI: https://doi.org/10.1007/s00607-018-0606-9

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