  The Data Mining process enables the end users to analyze, understand and use
the extracted knowledge in an intelligent system or to support in the
decision-making processes. However, many algorithms used in the process
encounter large quantities of patterns, complicating the analysis of the
patterns. This fact occurs with association rules, a Data Mining technique that
tries to identify intrinsic patterns in large data sets. A method that can help
the analysis of the association rules is the use of taxonomies in the step of
post-processing knowledge. In this paper, the GART algorithm is proposed, which
uses taxonomies to generalize association rules, and the RulEE-GAR
computational module, that enables the analysis of the generalized rules.
