A high efficient AprioriTid algorithm for mining association rule

Abstract

Mining association rule is one of the common forms in data mining, in which the critical problem is to gain the frequent itemsets efficiently. The classical Apriori and AprioriTid algorithm, which are used to construct the frequent itemset, are analyzed in this paper. Author finds out that there too many data due to those items repeatedly saved in the AprioriTid algorithm. On the basis of analysis, we give a theorem of the itemset whose support is less than minsup in C/sub k-1/ is useless in C/sub k-1/. Then, HEA algorithm based on the theorem is offered. The experiments show that the new algorithm is more effective in decreasing data size and execution times than AprioriTid algorithm.