Genetic-PSO Fuzzy Data Mining With Divide and Conquer Strategy

Nowadays, discovery the association rules is an important and controversial area in data mining research studies. These rules, describe noticeable association relationships among different attributes. While most studies have focused on binary valued transaction data, in real world applications, there data usually consist of quantitative values. With that in mind, in this paper, we propose a fuzzy data mining algorithm for extracting membership functions from quantitative transactions. This is a hybrid genetic-pso algorithm for finding membership functions suitable for mining problems by a strong cooperation of GA and PSO. This algorithm integrates the two techniques entire run of simulation in each iteration, a part of population are substituted by new ones generated by means of GA, while the other part is the same of previous generation but moved on the solution space by PSO. At the end, best final sets of membership functions in all the populations are gathered to be used for mining fuzzy association rules. According to experimental results, the proposed genetic-pso fuzzy data mining algorithm has a good effect on fitness of membership functions.