The Parameterized Complexity of Enumerating Frequent Itemsets

  • Matthew Hamilton
  • Rhonda Chaytor
  • Todd Wareham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4169)


A core problem in data mining is enumerating frequently-occurring itemsets in a given set of transactions. The search and enumeration versions of this problem have recently been proven NP- and #P-hard, respectively (Gunopulos et al, 2003) and known algorithms all have running times whose exponential terms are functions of either the size of the largest transaction in the input and/or the largest itemset in the output. In this paper, we analyze the complexity of the size-k frequent itemset enumeration problem relative to a variety of parameterizations. Many of our hardness results are proved using a recent extension of parameterized complexity to solution-counting problems (McCartin, 2002). These results include hardness for versions of this problem based on restricted transaction-set structure. We also derive a collection of fixed-parameter algorithms using off-the-shelf parameterized algorithm design techniques, several of which suggest new algorithmic directions for the frequent itemset enumeration problem.


Bipartite Graph Vertex Cover Frequent Itemset Truth Assignment Hardness Result 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Matthew Hamilton
    • 1
  • Rhonda Chaytor
    • 2
  • Todd Wareham
    • 2
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Computer ScienceMemorial University of NewfoundlandSt. John’sCanada

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