Comparative Study Over Classical Apriori and DSIM Methods

Fining frequent item set is a key issue in data mining; the Apriori algorithms use candidate itemsets to generate Frequent item set , but this approach is highly time-consuming because of self joining and prunining . To look for an algorithm that can avoid the generating of vast volume of candidate itemsets, DSIM (Data-Set Intersection Method) algorithm uses set intersection method to find the maximal frequent itemset.This process is performed by deleting items in infrequent 1-itemset and merging duplicate transaction repeatedly; the process is performed by generating intersections of transactions and deleting unneeded subsets recursively. This algorithm differs from all other methods which are used for discovering maximal frequent itemset.