Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
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Many studies have shown the limits of support/confidence framework used in Apriori-like algorithms to mine association rules. One solution to cope with this limitation is to get rid of frequent itemset mining and to focus as soon as possible on interesting rules. Many works have focussed on the algorithmic properties of the confidence. In particular, the all-confidence which is a transformation of the confidence, has the antimonotone property. In this paper, we generalize the all-confidence by associating to any measure its corresponding all-measure. We present a formal framework which allows us to make the link between analytic and algorithmic properties of the all-measure. We then propose the notion of all-monotony which corresponds to the monotony property of the all-measure. We show that there are 5 out of 37 measures which can be transformed into an antimonotone measure.