Abstract:

This paper presents a new hashing algorithm in discovering association rules among large data itemsets. Our approach scans the database once utilizing an enhanced version of priori algorithm, DHP. The algorithm includes two phases which compute the frequency of each k-itemsets and discover set of rules from frequent k-itemsets. Once the expert in the application domain provides the minimum support, the pruning phase is utilized to minimize the number of K-itemsets generated after completing the scanning of specific size database. The analysis of the algorithm shows