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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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Contributor : Christian Ernst Connect in order to contact the contributor
Submitted on : Monday, December 5, 2011 - 3:30:57 PM
Last modification on : Sunday, June 26, 2022 - 12:19:43 AM
Long-term archiving on: : Tuesday, March 6, 2012 - 2:35:47 AM


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  • HAL Id : emse-00648370, version 1


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. ⟨emse-00648370⟩



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