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KDD
2009
ACM

Correlated itemset mining in ROC space: a constraint programming approach

10 years 6 months ago
Correlated itemset mining in ROC space: a constraint programming approach
Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting correlation measures in ROC space and formulating correlated itemset mining as a constraint programming problem, we obtain new theoretical insights with practical benefits. More specifically, we contribute 1) an improved bound for correlated itemset miners, 2) a novel iterative pruning algorithm to exploit the bound, and 3) an adaptation of this algorithm to mine all itemsets on the convex hull in ROC space. The algorithm does not depend on a minimal frequency threshold and is shown to outperform several alternative approaches by orders of magnitude, both in runtime and in memory requirements. Categories and Subject Descriptors H.2.8 [Database Management]: Database applications-Data Mining; F.4.1 [Mathematical Logic and Formal Languages]: Mathematical Logic--Logic and Constraint Programming General Terms Algorithm...
Siegfried Nijssen, Tias Guns, Luc De Raedt
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors Siegfried Nijssen, Tias Guns, Luc De Raedt
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