摘要：The Data Mining process enables that end users can analyse, understand and use the extracted
knowledge in an intelligent system or to support decision processes. However, many algorithms used in
the process find 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
knowledge post-processing. In this paper, we propose the GART algorithm, which uses taxonomies to
generalize Association Rules, and the RulEE-GAR computational module, that enables the analysis of
the generalized rules.
关键词：Data Mining, Post-processing, Association Rules, Taxonomies.
