, Volume 100, Issue 4, pp 421–437 | Cite as

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

  • Qian Liu
  • Shameek Ghosh
  • Jinyan LiEmail author
  • Limsoon Wong
  • Kotagiri Ramamohanarao


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.


Pan-correlation pattern Time-course data Positive correlation patterns Negative correlation patterns Time-lagged positive correlation patterns Time-lagged negative correlation patterns 

Mathematics Subject Classification

68R01 (General) 


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Qian Liu
    • 1
  • Shameek Ghosh
    • 1
  • Jinyan Li
    • 1
    Email author
  • Limsoon Wong
    • 2
  • Kotagiri Ramamohanarao
    • 3
  1. 1.Advanced Analytics InstituteUniversity of Technology SydneyBroadwayAustralia
  2. 2.School of ComputingNational University of SingaporeSingaporeSingapore
  3. 3.Department of Computing and Information SystemsThe University of MelbourneParkvilleAustralia

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