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Conference papers

Multidimensional Association Rules in Boolean Tensors

Thi Kim Ngan Nguyen 1 Loic Cerf Marc Plantevit 1 Jean-François Boulicaut 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Popular data mining methods support knowledge discovery from patterns that hold in binary relations. We study the generalization of association rule mining within arbitrary n-ary relation and thus Boolean tensors instead of Boolean matrices. Indeed, many datasets of interest correspond to relations whose number of dimensions is greater or equal to 3. However, just a few proposals deal with rule discovery when both the head and the body can involve subsets of any dimensions. A challenging problem is to provide a semantics to such generalized rules by means of objective interestingness measures that have to be carefully designed. Therefore, we discuss the need for different generalizations of the classical confidence measure. We also present the first algorithm that computes, in such a general framework, every rule that satisfies both a minimal frequency constraint and minimal confidence constraints. The approach is tested on real datasets (ternary and 4-ary relations). We report on a case study that concerns dynamic graph analysis thanks to rules.
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Submitted on : Thursday, August 18, 2016 - 7:24:45 PM
Last modification on : Friday, December 11, 2020 - 12:36:02 PM

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Thi Kim Ngan Nguyen, Loic Cerf, Marc Plantevit, Jean-François Boulicaut. Multidimensional Association Rules in Boolean Tensors. 11th SIAM International Conference on Data Mining SDM'11, Apr 2011, Phoenix, Arizona, United States. pp.570-581, ⟨10.1137/1.9781611972818.49⟩. ⟨hal-01354377⟩



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