Evaluating generalized association rules through objective measures

Generalized association rules are rules that contain some background knowledge, therefore, giving a more general view of the domain. This knowledge is codified by a taxonomy set over the data set items. Many researches use taxonomies in different data mining steps to obtain generalized rules. In general, those researches reduce the obtained set by pruning some specialized rules using a subjective measure, but rarely analyzing the quality of the rules. In this context, this paper presents a quality analysis of the generalized association rules, where a different objective measure has to be used depending on the side a generalization item occurs. Based on this fact, a grouping measure was generated according to the generalization side. These measure groups can help the specialists to choose an appropriate measure to evaluate their generalized rules.