Mining Literal Correlation Rules from Itemsets

Abstract : Nowadays, data mining tools are becoming more and more popular to extract knowledge from a huge volume of data. In this paper, our aim is to extract Literal Correlation Rules: Correlation Rules admitting literal patterns given a set of items and a binary relation. If a pattern represents a valid Correlation Rule, then any literal belonging to its Canonical Base represents a valid Literal Correlation Rule. Moreover, in order to highlight only relevant Literal Correlation Rules, we add a pruning step based on a support threshold. To extract such rules, we modify the LHS-CHI2 Algorithm and perform some experiments.
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Communication dans un congrès
IMMM 2011 : The First International Conference on Advances in Information Mining and Management ISBN: 978-1-61208-162-5, Oct 2011, Barcelona, Spain. pp 162-167, 2011
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https://hal-emse.ccsd.cnrs.fr/emse-00648370
Contributeur : Christian Ernst <>
Soumis le : lundi 5 décembre 2011 - 15:30:57
Dernière modification le : mardi 23 octobre 2018 - 14:36:10
Document(s) archivé(s) le : mardi 6 mars 2012 - 02:35:47

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  • HAL Id : emse-00648370, version 1

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Alain Casali, Christian Ernst. Mining Literal Correlation Rules from Itemsets. IMMM 2011 : The First International Conference on Advances in Information Mining and Management ISBN: 978-1-61208-162-5, Oct 2011, Barcelona, Spain. pp 162-167, 2011. 〈emse-00648370〉

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