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Mining frequent itemsets: a perspective from operations research

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  • Wim Pijls
  • Walter A. Kosters

Abstract

Mining frequent itemsets is a flourishing research area. Many papers on this topic have been published and even some contests have been held. Most papers focus on speed and introduce ad hoc algorithms and data structures. In this paper we put most of the algorithms in one framework, using classical Operations Research paradigms such as backtracking, depth‐first and breadth‐first search and branch‐and‐bound. Moreover, we present experimental results where the different algorithms are implemented under similar designs.

Suggested Citation

  • Wim Pijls & Walter A. Kosters, 2010. "Mining frequent itemsets: a perspective from operations research," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(4), pages 367-387, November.
    • Handle: RePEc:bla:stanee:v:64:y:2010:i:4:p:367-387
    DOI: 10.1111/j.1467-9574.2010.00452.x
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