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Abstract

Association rule mining algorithms mostly use a randomly generated single seed to initialize a population without paying attention to the effectiveness of that population in evolutionary learning. Recently, research has shown significant impact of the initial population on the production of good solutions over several generations of a genetic algorithm. Single seed based genetic algorithms suffer from the following major challenges (1) solutions of a genetic algorithm are varied, since different seeds generate different initial population, (2) difficulty in defining a good seed for a specific application. To avoid these problems, in this paper we propose the MSGA, a new multiple seeds based genetic algorithm which generates multiple seeds from different domains of a solution space to discover high quality rules from a large data set. This scheme introduces m-domain model and m-seeds selection process through which the whole solution space is subdivided into m- number of same size domains, selecting a seed from each domain. Use of these seeds enables this method to generate an effective initial population for evolutionary learning of the fitness value of each rule. As a result, strong searching efficiency is obtained at the beginning of the evolution, achieving fast convergence. The MSGA is tested with different mutation and crossover operators for mining interesting Boolean association rules from four real world data sets. The results are compared to different single seeds based genetic algorithms under the same conditions.

Item Type:	Article
Authors/Creators:	Kabir, MMJ and Xu, S and Kang, BH and Zhao, Z
Keywords:	multiple seeds based genetic algorithm, initial population, Boolean association rules, conditional probability, search efficiency
Journal or Publication Title:	Expert Systems With Applications
Publisher:	Pergamon-Elsevier Science Ltd
ISSN:	0957-4174
DOI / ID Number:	https://doi.org/10.1016/j.eswa.2017.01.001
Copyright Information:	© 2017 Elsevier Ltd. All rights reserved.
Related URLs:	Google Scholar: Xu, S UTAS Profile: Xu, S Google Scholar: Kang, BH UTAS Profile: Kang, BH UTAS Profile: Zhao, Z
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