ExMiner: An Efficient Algorithm for Mining Top-K Frequent Patterns

  • Tran Minh Quang
  • Shigeru Oyanagi
  • Katsuhiro Yamazaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Conventional frequent pattern mining algorithms require users to specify some minimum support threshold. If that specified-value is large, users may lose interesting information. In contrast, a small minimum support threshold results in a huge set of frequent patterns that users may not be able to screen for useful knowledge. To solve this problem and make algorithms more user-friendly, an idea of mining the k-most interesting frequent patterns has been proposed. This idea is based upon an algorithm for mining frequent patterns without a minimum support threshold, but with a k number of highest frequency patterns. In this paper, we propose an explorative mining algorithm, called ExMiner, to mine k-most interesting (i.e. top-k) frequent patterns from large scale datasets effectively and efficiently. The ExMiner is then combined with the idea of “build once mine anytime” to mine top-k frequent patterns sequentially. Experiments on both synthetic and real data show that our proposed methods are more efficient compared to the existing ones.


Frequent Pattern Frequent Itemsets Support Threshold Explorative Mining Frequent Pattern Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tran Minh Quang
    • 1
  • Shigeru Oyanagi
    • 1
  • Katsuhiro Yamazaki
    • 1
  1. 1.Graduate school of Science and EngineeringRitsuimeikan UniversityKusatsu cityJapan

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