Title: Multi-criteria decisional approach for extracting relevant association rules

Authors: Addi Ait-Mlouk; Fatima Gharnati; Tarik Agouti

Addresses: Laboratory of Engineering and Information System, Department of Computer Science, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco ' Team of Telecommunications and Computer Networks, Department of Physics, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco ' Laboratory of Engineering and Information System, Department of Computer Science, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco

Abstract: Association rule mining plays a vital role in knowledge discovery in databases. The difficult task is mining useful and non-redundant rules, in fact in most cases, the real datasets lead to a huge number of rules, which does not allow users to make their own selection of the most relevant. Several techniques are proposed such as rule clustering, informative cover method, quality measurements, etc. Another way to selecting relevant association rules, we believe it is necessary to integrate a decisional approach within the knowledge discovery process; to solve the problem, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis (MCA) by using association rules as actions and quality measurements as criteria. Finally, we conclude our work by an empirical study to illustrate the performance of our proposed approach.

Keywords: data mining; knowledge discovery in database; association rules; quality measurements; multi-criteria analysis; MCA; decision-making system; ELECTRE TRI.

DOI: 10.1504/IJCSE.2017.087402

International Journal of Computational Science and Engineering, 2017 Vol.15 No.3/4, pp.188 - 200

Received: 01 Mar 2016
Accepted: 09 Aug 2016
Published online: 15 Oct 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article