A Novel Efficient Data Structure to Mine Frequent Itemset

Association rule mining is to extract the interesting correlation and relation between the large volumes of databases. Association rule mining process is divided into two sub problem: The first problem is to find the frequent itemsets from the transaction and second problem is to construct the rule from the mined frequent itemset. Frequent itemsets generation is the prerequisite and most time overwhelming process for association rule mining. Apriori algorithm is the familiar and fundamental algorithm to generate the frequent itemsets from the transaction sets. Till now, Lot of researcher modified the Apriori in various manner like partition approach, Hash function and etc. But most efficient Apriori-like algorithms rely heavily on the minimum support constraints to prune the vast amount of non-candidate itemsets. These algorithms store many unwanted itemsets and transactions. In this paper propose a novel frequent itemsets generation algorithm. The drawback of the HEA, AprioriTId and Apriori overcome by the proposed algorithm. The proposed algorithm is an improved version of High Efficient AprioriTid (HEA) algorithm. The proposed algorithm is using the two theorems which are proposed in this paper. The proposed algorithm is tested with the synthetic retail dataset. It performed well at low supports. The experimental reports also show that proposed algorithm on an outset is faster than HEA, AprioriTID and Apriori.