Authors:
Rafael Garcia Leonel Miani
1
and
Estevam Rafael Hruschka Junior
2
Affiliations:
1
Federal Institute of Sao Paulo, Brazil
;
2
Federal University of Sao Carlos, Brazil
Keyword(s):
Association Rules, Irrelevant Rules, Large Knowledge Bases, Redundant Rules.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Data Mining
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Large Scale Databases
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Large growing knowledge bases are being an explored issue in the past few years. Most approaches focus
on developing techniques to increase their knowledge base. Association rule mining algorithms can also
be used for this purpose. A main problem on extracting association rules is the effort spent on evaluating
them. In order to reduce the number of association rules discovered, this paper presents ER component,
which eliminates the extracted rules in two ways at the post-processing step. The first introduces the concept
of super antecedent rules and prunes the redundant ones. The second method brings the concept of super
consequent rules, eliminating those irrelevant. Experiments showed that both methods combined can decrease
the amount of rules in more than 30%. We also compared ER to FP-Growth, CHARM and FPMax algorithms.
ER generated more relevant and efficient association rules to populate the knowledge base than FP-Growth,
CHARM and FPMax.