Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Since then, there has been considerable work on designing algorithms for mining such rules. In recent years, the techniques of association rule mining have been applied to gene expression data analysis to reveal relationships between genes and different conditions and features. In addition, different features and conditions have been used to extract interesting patterns from gene expression datasets. The existing system is a basic algorithm for learning association rules to control on databases that have transactions. But, it has been noticed that it is very difficult to reduce all such limitations simultaneously. In this project the system proposed a weighted rule-mining technique called RANWAR. The RANWAR algorithm is basically updated version of Apriori algorithm with the weighted measures. It generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. The performance of the proposed system compare to the existing rule mining techniques. It saves time of execution of the algorithm. The main objective of the system is to reduce elapsed time for rule mining.