An Optimization of Association Rule Mining using K-Map and Genetic Algorithm for Large Database

Abstract

Rule mining is very efficient technique for find relation of correlated data. The correlation of data gives meaning full extraction process. For the mining of rule mining a variety of algorithm are used such as Apriori algorithm and tree based algorithm. Some algorithm is wonder performance but generate negative association rule and also suffered from multi-scan problem. In this paper we proposed a k-apriori-GA association rule mining based on genetic algorithm and K-map formula. In this method we used a k-map binary table for partition of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of kpartition and near distance of k-map candidate key. Support weight key is a vector value given by the transaction data set. The process of rule optimization we used genetic algorithm and for evaluate algorithm conducted the real world dataset The National Rural Employment Guarantee Act (NREGA) Department of Rural Development Government of India.