Veronica Oliveira de Carvalho, Fabiano Fernandes dos Santos, Solange Oliveira Rezende


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 minimizes the user’s effort during the post-processing process.


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Paper Citation

in Harvard Style

Carvalho V., Santos F. and Rezende S. (2011). POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-53-9, pages 54-63. DOI: 10.5220/0003457500540063

in Bibtex Style

author={Veronica Oliveira de Carvalho and Fabiano Fernandes dos Santos and Solange Oliveira Rezende},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
SN - 978-989-8425-53-9
AU - Carvalho V.
AU - Santos F.
AU - Rezende S.
PY - 2011
SP - 54
EP - 63
DO - 10.5220/0003457500540063