An efficient algorithm for mining frequent closed itemset

Association rules mining was an important field of data mining research. Discovering the potential frequent itemset was a key step in it. The existed frequent itemset discovery algorithms could discover all the frequent itemset or maximal frequent itemset. N. Pasquier proposed a new task of mining frequent closed itemset. The size of frequent closed itemset was much smaller than all the frequent itemsets and did not lose any information. In this paper a new frequent closed itemset algorithm based on the directed itemset graph is given. This algorithm can discover all the frequent closed itemset efficiently by using depth first search strategy. The experiment shows that it is efficient for mining frequent closed itemsets.