Efficient Mining of Frequent Itemsets in Distorted Databases

  • Jinlong Wang
  • Congfu Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


Recently, the data perturbation approach has been applied to data mining, where original data values are modified such that the reconstruction of the values for any individual transaction is difficult. However, this mining in distorted databases brings enormous overheads as compared to normal data sets. This paper presents an algorithm GrC-FIM, which introduces granular computing (GrC), to address the efficiency problem of frequent itemset mining in distorted databases. Using the key granule concept and granule inference, support counts of candidate non-key frequent itemsets can be inferred with the counts of their frequent sub-itemsets obtained during an earlier mining. This eliminates the tedious support reconstruction for these itemsets. And the accuracy is improved in dense data sets while that in sparse ones is the same.


Association Rule Minimum Support Frequent Itemsets Original Database Support Count 
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

  • Jinlong Wang
    • 1
  • Congfu Xu
    • 1
  1. 1.Institute of Artificial IntelligenceZhejiang UniversityHangzhouChina

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