Mining Non-redundant Periodic Frequent Patterns

  • Michael Kofi Afriyie
  • Vincent Mwintieru NofongEmail author
  • John Wondoh
  • Hamidu Abdel-Fatao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)


Discovering periodic frequent patterns has been useful in various decision making. Traditional algorithms, however, often report a large number of such patterns, most of which are often redundant since their periodic occurrences can be inferred from other periodic frequent patterns. Employing such redundant periodic frequent patterns in decision making would often be detrimental if not trivial. To address this challenge and report only non-redundant periodic frequent patterns, this paper employs the concept of deduction rules in mining the set of non-redundant periodic frequent patterns. A Non-redundant Periodic Frequent Pattern Miner (NPFPM) is subsequently proposed to achieve this purpose. Experimental analysis on benchmark datasets show that NPFPM is efficient and can effectively prune the set of redundant periodic frequent patterns.


Frequent patterns Periodic frequent patterns Non-redundance 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Michael Kofi Afriyie
    • 1
  • Vincent Mwintieru Nofong
    • 1
    Email author
  • John Wondoh
    • 2
  • Hamidu Abdel-Fatao
    • 1
  1. 1.University of Mines and TechnologyTarkwaGhana
  2. 2.University of South of AustraliaAdelaideAustralia

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