Sciweavers

Share
SDM
2011
SIAM

Multidimensional Association Rules in Boolean Tensors

3 years 3 months ago
Multidimensional Association Rules in Boolean Tensors
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 relations 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 ...
Kim-Ngan Nguyen, Loïc Cerf, Marc Plantevit, J
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SDM
Authors Kim-Ngan Nguyen, Loïc Cerf, Marc Plantevit, Jean-François Boulicaut
Comments (0)
books