A new approach of modified transaction reduction algorithm for mining frequent itemset
Ramaraj Eswara Thevar, R. Krishnamoorthy · 2008
Association rule mining is to extract the interesting correlation and relation between the large volumes of transactions. This process is divided into two sub problem: 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 requirement and most time vast process for association rule mining. Nowadays, 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 frequency itemsets generation algorithm called MTR-FMA (Modified Transaction Reduction based Frequent Itemset Mining algorithm) that maintains its performance even at relative low supports. The experimental reports also show that proposed MTR-FMA algorithm on an outset is faster than High Efficient AprioriTid and other some algorithms.