On Exploring the Power-Law Relationship in the Itemset Support Distribution

  • Kun-Ta Chuang
  • Jiun-Long Huang
  • Ming-Syan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


We identify and explore in this paper an important phenomenon which points out that the power-law relationship appears in the distribution of itemset supports. Characterizing such a relationship will benefit many applications such as providing the direction of tuning the performance of the frequent-itemset mining. Nevertheless, due to the explosive number of itemsets, it will be prohibitively expensive to retrieve characteristics of the power-law relationship in the distribution of itemset supports. As such, we also propose in this paper a valid and cost-effective algorithm, called algorithm PPL, to extract characteristics of the distribution without the need of discovering all itemsets in advance. Experimental results demonstrate that algorithm PPL is able to efficiently extract the characteristics of the power-law relationship with high accuracy.


Association Rule Real Dataset Minimum Support Frequent Itemsets Mining Association Rule 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kun-Ta Chuang
    • 1
  • Jiun-Long Huang
    • 2
  • Ming-Syan Chen
    • 1
  1. 1.Graduate Institute of Communication EngineeringNational Taiwan UniversityTaipeiTaiwan, ROC
  2. 2.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan, ROC

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