Maximal Frequent Itemsets Mining Using Database Encoding

Frequent itemsets mining is a classic problem in data mining and plays an important role in data mining research for over a decade. However, the mining of the all frequent itemsets will lead to a massive number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets. In this paper, we propose a new method for mining maximal frequent itemsets. Our method introduces an efficient database encoding technique, a novel tree structure called PC_Tree and also PC_Miner algorithm. The database encoding technique utilizes Prime number characteristics and transforms each transaction into a positive integer that has all properties of its items. The PC_Tree is a simple tree structure but yet powerful to capture whole of transactions by one database scan. The PC_Miner algorithm traverses the PC_Tree to mine maximal frequent itemsets. Experiments verify the efficiency and advantages of the