A SYSTEMATIC APPROACH AND ALGORITHM FOR FREQUENT DATA ITEMSETS

The research in data mining is developing fast and efficient algorithm to derive knowledge from huge databases. There are several data mining algorithms available to solve diverse data mining problems. They are mainly classified as Associations, Classifications, Sequential Patterns and Clustering. Apriori is one of the most important algorithms used in Rule Association Mining. In this paper, we discuss the limitations of the Existing Apriori algorithm and then propose an enhancement for improving its efficiency. The drawbacks of the existing system may produce a larger number of candidate item sets and scan the database many times. The proposed algorithm is based on the reverse scan of a given database. If certain conditions are satisfied, the proposed algorithm can greatly reduce the scanning times required for the discovery of candidate itemsets. Therefore, much time and space has been saved while searching frequent itemsets