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Conference Papers Year : 2019

Constraint Programming for Association Rules

Abstract

Discovering association rules among items in a dataset is one of the fundamental problems in data mining. It has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper we propose a declarative model based on constraint programming to capture association rules. Our model also allows us to specify any additional property and/or user's constraints on the kind of rules the user is looking for. To implement our model, we introduce a new global constraint, Confident, for ensuring the confidence of rules. We prove that completely propagating Confident is NP-hard. We thus provide a decomposition of Confident. In addition to user's constraints on the items composing body and head of the rules, we show that we can capture the popular minimal non-redundant property of association rules. An experimental analysis shows the practical effectiveness of our approach compared to existing approaches.
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Dates and versions

lirmm-02089719 , version 1 (04-04-2019)

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Mohamed-Bachir Belaid, Christian Bessiere, Nadjib Lazaar. Constraint Programming for Association Rules. SDM: SIAM International Conference on Data Mining, May 2019, Calgary, Canada. pp.127-135, ⟨10.1137/1.9781611975673.15⟩. ⟨lirmm-02089719⟩
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