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.
Type de document :
Communication dans un congrès
DOLAP'07: ACM Tenth International Workshop on Data Warehousing and OLAP, Nov 2007, Lisbon, Portugal, pp.12, 2007
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00175246
Contributeur : Marc Plantevit <>
Soumis le : jeudi 27 septembre 2007 - 13:18:14
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00175246, version 1

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Marc Plantevit, Sabine Goutier, Françoise Guisnel, Anne Laurent, Maguelonne Teisseire. Mining Unexpected Multidimensional Rules. DOLAP'07: ACM Tenth International Workshop on Data Warehousing and OLAP, Nov 2007, Lisbon, Portugal, pp.12, 2007. 〈lirmm-00175246〉

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