Semantics-based classification of rule interestingness measures

Julien Blanchard (1) , Fabrice Guillet (1) , Pascale Kuntz (1)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index.

Mots clés: Association rules; Interestingness measures classification

Domaines: Apprentissage [cs.LG]

Julien Blanchard, Fabrice Guillet, Pascale Kuntz. Semantics-based classification of rule interestingness measures. Yanchang Zhao, Chengqi Zhang, Longbing Cao. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, IGI Global, pp.56-79, 2009. ⟨hal-00420971⟩