Mining rules: A parallel multiobjective particle swarm optimization approach

Abstract

Data mining is the overall process of extracting knowledge from data. In the study of how to represent knowledge in data mining context, rules are one of the most used representation form. However, the first issue in data mining is the computational complexity of the rule discovery process due to the huge amount of data. In this sense, this chapter proposes a novel approach based on a previous work that explores Multi-Objective Particle Swarm Optimization (MOPSO) in a rule learning context, called MOPSO-N. MOPSO-N applies MOPSO to search for rules with specific properties exploring Pareto dominance concepts. Besides, these rules can be used as an unordered classifier, so the rules are more intuitive and easier to understand because they can be interpreted independently one of the other. In this chapter, first some extensions to MOPSO-N are presented. These extensions are enhancements to the original algorithm to increase its performance, and to validate them, a wide set of experiments is conducted. Second, the main goal of this chapter, the parallel approach of MOPSO-N, called MOPSO-P, is described. MOPSO-P allows the algorithm to be applied to large datasets. The proposed MOPSO-P is evaluated, and the results showed that MOPSO-P is efficient for mining rules from large datasets. © 2009 Springer-Verlag Berlin Heidelberg.