Optimization and Improvement of Association Rule Mining using Genetic Algorithm and Fuzzy Logic

Abstract

In Association rule mining, association rules grows exponentially with the size of the frequent itemset. With so many association rules one is interested in the optimized rules to make decisions. With huge attribute’s domain, the rules generated in the association rule mining are also huge. In this paper, the main focus is on how to optimize the association rules using Genetic Algorithm and How to improve the working of Association Rule Mining using Fuzzy Logic. With explosion of data, optimization of the results is as important as producing the results. Genetic Algorithm perform global search to find attribute interaction than the greedy rule induction algorithm often used in Data Mining. To show the use of fuzzy logic and Genetic Algorithm in Association Rule Mining, a system to predict the chance of having heart disease is considered in this paper.