POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES
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Data
2011-01-01
Autores
Carvalho, Veronica Oliveira de [UNESP]
Santos, Fabiano Fernandes dos
Rezende, Solange Oliveira
Zhang, R.
Cordeiro, J.
Li, X
Zhang, Z.
Zhang, J.
Título da Revista
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Título de Volume
Editor
Insticc-inst Syst Technologies Information Control & Communication
Resumo
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 minimises the user's effort during the post-processing process.
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Palavras-chave
Association rules, Post-processing, Clustering and objective measures
Como citar
Iceis 2011: Proceedings Of The 13th International Conference On Enterprise Information Systems, Vol 1. Setubal: Insticc-inst Syst Technologies Information Control & Communication, p. 54-63, 2011.