Mining & Optimization of Association Rules Using Effective Algorithm

Association Rule Mining is generally performed in Generation of frequent item sets & Rule generation. Mining association rules is not full of reward until it can be utilized to improve decision-making process of an organization. This paper is concerned with discovering positive and negative association rules. We present an Apriori-based algorithm that is able to find all valid positive and negative association rules in a support confidence framework. The algorithm can find all valid association rules quickly and overcome some limitations of the previous mining methods. The complexity and large size of rules generated after mining have motivated researchers and practitioners to optimize the rule, for analysis purpose. This optimization can be done using Genetic Algorithm. Keywords—association rule, frequent item set, correlation coefficient, optimization, genetic algorithm.