Comparative Analysis of Association Rule Mining Based on Genetic Algorithm

Abstract

Association rule mining play an important role in various data mining process. The diversity of association rule mining spread in various field such as market bucket analysis, medical diagnose and share market prediction. Now a days various authors and researcher focus on validation of association rule mining. For the validation of association rule mining used various optimization algorithm are used such as genetic algorithm, Ant Colony Optimization and particle of swarm optimization also used. 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 proposed multi-level minimum supports (MLMS-GA) association rule mining based on min-max algorithm and MLMS formula. In this method we used a multi-level minimum supports of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of MLMS and min-max algorithm. Support length key is a vector value given by the transaction data set. The process of rule optimization we used min-max algorithm and for evaluate algorithm conducted the real-world dataset such as heart disease data and some standard data used from UCI machine learning repository.