An Hybrid Optimization Algorithm for Fuzzy Association rule Mining

Fuzzy Association rule mining is an essential topic in Information retrieval mining field. It produces all important Fuzzy association rules between attributes in the dataset because large data set records considered as transactions. Each transaction consists of set of attributes. Fuzzy based Apriori algorithm produces all important Fuzzy association rules between attributes in the dataset. On the basis of the Fuzzy association rule mining and Fuzzy Apriori algorithm, the proposed work adapted an improved algorithm based on Artificial Bee Colony Optimization. It is not possible to justify that all the rules generated by Fuzzy based Apriori algorithm produce optimum result. Thus optimization of the result generated was carried out by Fuzzy Apriori algorithm using Fuzzy Artificial bee colony optimization (FABCO), its worthy noting that a significant findings were revealed. FABCO is used for optimization of rules to get the best classification accuracy. The proposed method was compared with the traditional artificial bee colony optimization and the particle swarm optimization. The current work proved a better classification performance compared to un-pruned rules.