The parameterized complexity of enumerating frequent itemsets

  • Authors:
  • Matthew Hamilton;Rhonda Chaytor;Todd Wareham

  • Affiliations:
  • Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada;Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada

  • Venue:
  • IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.