BitApriori: An Apriori-Based Frequent Itemsets Mining Using Bit Streams
Thi Thanh Nhan Le, Thị Thanh Thủy Nguyễn, Tae Chong Chung · 2010
Generating, pruning and counting itemset candidates are important steps in Apriori frequent itemset mining. Unfortunately, their computation time are too expensive. In this paper, we propose a new method using Bit Stream to improve their speed. At the begining, the 1-itemsets are found out and sorted according to the decline of count. By that way, a map of all attributes would be created. After that, each attribute will be presented by 1 bit. At last, the generating and pruning itemset candidates are processed by LOGIC operations which are not cost much of computation time. For experiments we compare our method with some Apriori-based state of the arts.