Mining frequent itemsets: a perspective from operations research
Article first published online: 17 MAY 2010
© 2010 The Authors. Statistica Neerlandica © 2010 VVS
Volume 64, Issue 4, pages 367–387, November 2010
How to Cite
Pijls, W. and Kosters, W. A. (2010), Mining frequent itemsets: a perspective from operations research. Statistica Neerlandica, 64: 367–387. doi: 10.1111/j.1467-9574.2010.00452.x
- Issue published online: 17 MAY 2010
- Article first published online: 17 MAY 2010
- Received: April 2009. Revised: February 2010.
- data mining;
- operations research;
- frequent itemsets;
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.