An Analytical Evaluation of Objective Measures Behavior for Generalized Association Rules

The association rule mining task identifies all the intrinsic associations among the items contained in data and leads to only specialized knowledge. To overcome this problem the generalized association rules appeared. This type of rule associates not only the items contained in data, but also some items encoded into a given taxonomy. Therefore, the techniques used to obtain generalized association rules are very useful since they provide a more general view of the domain. However, a problem found when using these techniques is how to identify the most useful rules to avoid overload the user with a huge amount of patterns. Nowadays, the researches use objective evaluation measures to evaluate and select the most interesting knowledge to the user. Despite the fact these measures have been studied by many researches to evaluate many types of rules (for example, classification and traditional association rules), it is important to study these measures in the context of generalized rules. Thus, this paper presents an analytical evaluation to understand the behavior of some objective measures when applied in a set of generalized rules. Many relations were obtained to express the behavior of these measures, what represents a meaningful contribution to the post-processing data mining area