Objectively evaluating interestingness measures for frequent itemset mining

Abstract

Itemset mining approaches, while having been studied for more than 15 years, have been evaluated only on a handful of data sets. In particular, they have never been evaluated on data sets for which the ground truth was known. Thus, it is currently unknown whether itemset mining techniques actually recover underlying patterns. Since the weakness of the algorithmically attractive support/confidence framework became apparent early on, a number of interestingness measures have been proposed. Their utility, however, has not been evaluated, except for attempts to establish congruence with expert opinions. Using an extension of the Quest generator proposed in the original itemset mining paper, we propose to evaluate these measures objectively for the first time, showing how many non-relevant patterns slip through the cracks. © Springer-Verlag 2013.