Abstract
The set of frequent closed itemsets determines exactly the complete set of all frequent itemsets and is usually much smaller than the latter. This paper proposes an improved algorithm for mining frequent closed itemsets. Firstly, the index array is proposed, which is used for discovering those items that always appear together. Then, by using bitmap, an algorithm for computing index array is presented. Thirdly, based on the heuristic information provided by index array, frequent items, which co-occur together and share the same support, are merged together. Thus, initial generators are calculated. Finally, based on index array, reduced pre-set and reduced post-set are proposed. It is proved that the reduced pre-set and reduced post-set not only retain the function of pre-set and post-set, but also have smaller sizes. Therefore, the redundant items in pre-set and post-set are deleted, thus making it possible to save a lot of work related to inclusion check. The experimental results show that the proposed algorithm is efficient especially on dense dataset.