Closed Non-derivable Itemsets

  • Juho Muhonen
  • Hannu Toivonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


Itemset mining typically results in large amounts of redundant itemsets. Several approaches such as closed itemsets, non-derivable itemsets and generators have been suggested for losslessly reducing the amount of itemsets. We propose a new pruning method based on combining techniques for closed and non-derivable itemsets that allows further reductions of itemsets. This reduction is done without loss of information, that is, the complete collection of frequent itemsets can still be derived from the collection of closed non-derivable itemsets. The number of closed non-derivable itemsets is bound both by the number of closed and the number of non-derivable itemsets, and never exceeds the smaller of these. Our experiments show that the reduction is significant in some datasets.


Association Rule Frequent Itemsets Support Threshold Frequent Itemset Mining Deduction Rule 
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

  • Juho Muhonen
    • 1
  • Hannu Toivonen
    • 1
  1. 1.Helsinki Institute for Information Technology, Basic Research Unit, Department of Computer ScienceUniversity of HelsinkiFinland

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