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Objectively evaluating condensed representations and interestingness measures for frequent itemset mining

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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.

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Acknowledgments

We are grateful to Christian Borgelt and Tijl De Bie for their support w.r.t. the FPGrowth implementation and the MaxEnt Database Generator, respectively, and to our colleagues Matthijs van Leeuwen and Tias Guns, and the participants of Qimie 2013 for helpful discussions. Finally, we thank the anonymous reviewers for their help in improving the manuscript. The author is supported by a post-doctoral grant by the Fonds Wetenschappelijk Onderzoek Vlanderen (FWO).

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  1. KU Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium

    Albrecht Zimmermann

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  1. Albrecht Zimmermann
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Zimmermann, A. Objectively evaluating condensed representations and interestingness measures for frequent itemset mining. J Intell Inf Syst 45, 299–317 (2015). https://doi.org/10.1007/s10844-013-0297-9

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