Multicriteria attractiveness evaluation of decision and association rules

Abstract

The work is devoted to multicriteria approaches to rule evaluation. It analyses desirable properties (in particular the property M, property of confirmation and hypothesis symmetry) of popular interestingness measures of decision and association rules. Moreover, it analyses relationships between the considered interestingness measures and enclosure relationships between the sets of non-dominated rules in different evaluation spaces. It's main result is a proposition of a multicriteria evaluation space in which the set of non-dominated rules will contain all optimal rules with respect to any attractiveness measure with the property M. By determining the area of rules with desirable value of a confirmation measure in the proposed multicriteria evaluation space one can narrow down the set of induced rules only to the valuable ones. Furthermore, the work presents an extension of an apriori-like algorithm for generation of rules with respect to attractiveness measures possessing valuable properties and shows some applications of the results to analysis of rules induced from exemplary datasets. © 2009 Springer.