Knowledge and Information Systems

, Volume 43, Issue 3, pp 497–527 | Cite as

PGLCM: efficient parallel mining of closed frequent gradual itemsets

  • Trong Dinh Thac Do
  • Alexandre Termier
  • Anne Laurent
  • Benjamin Negrevergne
  • Behrooz Omidvar-Tehrani
  • Sihem Amer-Yahia
Regular Paper


Numerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: “When X increases, Y decreases”. There exist some algorithms for mining frequent gradual patterns, but they cannot scale to real-world databases. We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent gradual itemsets. Our experimental study shows that GLCM is two orders of magnitude faster than the state of the art, with a constant low memory usage. We also present PGLCM, a parallelization of GLCM capable of exploiting multicore processors, with good scale-up properties on complex datasets. These algorithms are the first algorithms capable of mining large real world datasets to discover gradual patterns.


Data mining Frequent pattern mining Gradual itemsets Parallelism 


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Trong Dinh Thac Do
    • 1
    • 2
  • Alexandre Termier
    • 1
  • Anne Laurent
    • 2
  • Benjamin Negrevergne
    • 3
  • Behrooz Omidvar-Tehrani
    • 1
  • Sihem Amer-Yahia
    • 1
  1. 1.LIG, CNRS UMRUniversity of GrenobleGrenobleFrance
  2. 2.LIRMM, CNRS UMRUniversity of Montpellier IIMontpellierFrance
  3. 3.Department of Computer ScienceKU LeuvenLeuvenBelgium

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