An Efficient Algorithm for Finding All Frequent Itemsets

Frequent itemsets are crucial to many tasks in data mining. A new algorithm for finding all frequent itemsets is proposed in this paper. In data mining, the process of counting any itemset´s support requires a great I/O and computing cost. An impacted bitmap technique to speed up the counting process is employed in this paper. Nevertheless, saving the intact bitmap usually has a big space requirement. In this algorithm, each bit vector is partitioned into some blocks, and hence every bit block is encoded as a shorter symbol. Therefore the original bitmap is impacted efficiently. And then the algorithm converts the origin transaction database to an adjacent-itemsets-lattice (which is a directed graph) in a preprocessing, where each itemset vertex has a label to represent its support. So we can change the complicated task of mining frequent itemsets in the database to a simpler one of searching vertexes in this structure, which can speeds up greatly the mining process. At the end experimental and analytical results are presented