An efficient algorithm for frequent itemsets in data mining
Jiemin Zheng, Defu Zhang, Stephen C.H. Leung, Xiyue Zhou · 2010
Mining frequent itemsets is one of the most investigated fields in data mining. It is a fundamental and crucial task. Apriori is among the most popular algorithms used for the problem but support count is very time-consuming. In order to improve the efficiency of Apriori, a novel algorithm, named BitApriori, for mining frequent itemsets, is proposed. Firstly, the data structure binary string is employed to describe the database. The support count can be implemented by performing the Bitwise "And" operation on the binary strings. Another technique for improving efficiency in BitApriori presented in this paper is a special equal-support pruning. Experimental results show the effectiveness of the proposed algorithm, especially when the minimum support is low.