Mining Unexpected Multidimensional Rules

Marc Plantevit 1 Sabine Goutier 2 Françoise Guisnel 2 Anne Laurent 1 Maguelonne Teisseire 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Discovering unexpected rules is essential, particularly for in- dustrial applications with marketing stakes. In this context, many works have been done for association rules. How- ever, non of them address sequences. In this paper, we thus propose to discover unexpected multidimensional sequential rules in data cubes. We define the concept of multidimen- sional sequential rule, and then unexpectedness. We formal- ize these concepts and define an algorithm for mining this kind of rules. Experiments on a real data cube are reported and highlight the interest of our approach.
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Marc Plantevit, Sabine Goutier, Françoise Guisnel, Anne Laurent, Maguelonne Teisseire. Mining Unexpected Multidimensional Rules. DOLAP: Data Warehousing and OLAP, Nov 2007, Lisbonne, Portugal. pp.89-96, ⟨10.1145/1317331.1317347⟩. ⟨lirmm-00175246⟩

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