A New Algorithm for Discovery Maximal Frequent Itemsets Based on Binary Vector Sets
Xin Jing-wei, Yang Guo-qiang, Jizhou Sun, Yaping Zhang · 2006
Frequent itemset mining is a classic problem in data mining. However, most algorithms have to scan databases many times. This paper presents an algorithm that can find maximal frequent itemsets quickly. In this algorithm, each transaction is represented as a binary vector, so the task of discovering maximal frequent itemsets is turn to search frequent patterns in binary vector set. The algorithm is unique in that it simultaneously explores both the itemset space and transaction space, unlike previous frequent itemset mining methods that only exploit the itemset search space. Furthermore, this algorithm can certify mining maximal frequent patterns with only one scan of original databases. Experiments verify the efficiency and advantages of the proposed algorithm