GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets

Anne Laurent 1 Marie-Jeanne Lesot 2 Maria Rifqi 2
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Gradual dependencies of the form the more A, the more B offer valuable information that linguistically express relationships be- tween variations of the attributes. Several formalisations and automatic extraction algorithms have been proposed recently. In this paper, we first present an overview of these methods. We then propose an algorithm that combines the principles of several existing approaches and benefits from efficient computational properties to extract frequent gradual itemsets.
Type de document :
Communication dans un congrès
Eighth International Conference on Flexible Query Answering Systems (FQAS 2009), Oct 2009, Roskilde, Denmark. Springer-Verlag, 5822, pp.382-393, 2009, Lecture Notes in Computer Science. 〈http://fqas2009.org〉. 〈10.1007/978-3-642-04957-6_33〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00408735
Contributeur : Anne Laurent <>
Soumis le : dimanche 2 août 2009 - 16:31:15
Dernière modification le : mercredi 21 novembre 2018 - 15:18:07

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Anne Laurent, Marie-Jeanne Lesot, Maria Rifqi. GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets. Eighth International Conference on Flexible Query Answering Systems (FQAS 2009), Oct 2009, Roskilde, Denmark. Springer-Verlag, 5822, pp.382-393, 2009, Lecture Notes in Computer Science. 〈http://fqas2009.org〉. 〈10.1007/978-3-642-04957-6_33〉. 〈lirmm-00408735〉

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