Mining Strong Valid Association Rule from Frequent Pattern and Infrequent Pattern Based on Min-Max Sinc Constraints

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 IMLMS-PANR-GA association rule mining based on min-max algorithm and MLMS formula. In this method we used a multi-level multiple support 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.