Sciweavers

Share
ICEIS
2009
IEEE

NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies

9 years 1 months ago
NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies
Traditional approaches for mining generalized association rules are based only on database contents, and focus on exact matches among items. However, in many applications, the use of some background knowledge, as ontologies, can enhance the discovery process and generate semantically richer rules. In this way, this paper proposes the NARFO algorithm, a new algorithm for mining non-redundant and generalized association rules based on fuzzy ontologies. Fuzzy ontology is used as background knowledge, to support the discovery process and the generation of rules. One contribution of this work is the generalization of non-frequent itemsets that helps to extract important and meaningful knowledge. NARFO algorithm also contributes at post-processing stage with its generalization and redundancy treatment. Our experiments showed that the number of rules had been reduced considerably, without redundancy, obtaining 63.63% average reduction in comparison with XSSDM algorithm.
Rafael Garcia Miani, Cristiane A. Yaguinuma, Maril
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICEIS
Authors Rafael Garcia Miani, Cristiane A. Yaguinuma, Marilde Terezinha Prado Santos, Mauro Biajiz
Comments (0)
books