2011/12/07 by Marcos Aurélio Domingues, Domingues, Marcos Aurélio, Solange Oliveira Rezende +1
Computer Science · #Databases (cs.DB) #FOS: Computer and information sciences #I.2.6 #Machine Learning (cs.LG) #cs.DB #cs.LG
paper · pdf · doi:10.48550/arxiv.1112.1734
ECML/PKDD'05 The Second International Workshop on Knowledge Discovery and Ontologies (KDO'05)
arxiv created 2011/12/07 · arxiv updated 2011/12/09
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.