Abstract: Aiming at the problem that FBCM (FUP (fast update algorithm) based on matrix compression) algorithm frequently scanned the original frequent itemset library and generated a large number of candidate sets in the process of item set mining. We proposed a method to extract the most frequent items from the database to reduce the scanning times on the original frequent itemset library, and through a candidate set pruning idea, it reduced the generation of candidate sets in the whole running process of the algorithm, so as to improve the speed of frequent itemset mining. Experimental results show that the efficiency of the algorithm is 15% higher than that of FBCM algorithm, and the highest is 60% under the same experimental conditions.

Key words: association rule, incremental mining, candidate set pruning, most frequent item