Authors:
Veronica Oliveira de Carvalho
1
;
Fabiano Fernandes dos Santos
2
and
Solange Oliveira Rezende
2
Affiliations:
1
UNESP - Univ Estadual Paulista, Brazil
;
2
USP - Universidade de São Paulo, Brazil
Keyword(s):
Association rules, Post-processing, Clustering and objective measures.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
The post-processing of association rules is a difficult task, since a large number of patterns can be obtained. Many approaches have been developed to overcome this problem, as objective measures and clustering, which are respectively used to: (i) highlight the potentially interesting knowledge in domain; (ii) structure the domain, organizing the rules in groups that contain, somehow, similar knowledge. However, objective measures don’t reduce nor organize the collection of rules, making the understanding of the domain difficult. On the other hand, clustering doesn’t reduce the exploration space nor direct the user to find interesting knowledge, making the search for relevant knowledge not so easy. This work proposes the PAR-COM (Post-processing Association
Rules with Clustering and Objective Measures) methodology that, combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. Thereby, PAR-COM mi
nimizes the user’s effort during the post-processing process.
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