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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.

Association rule mining is to find association relationships among large data sets. Mining frequent patterns is an important aspect in association rule mining. In this paper, an efficient algorithm named apriori-growth based on apriori algorithm and the FP-tree structure is presented to mine frequent patterns. The advantage of the apriori-growth algorithm is that it doesn't need to generate conditional pattern bases and sub-conditional pattern tree recursively. Computational results show the apriori-growth algorithm performs faster than apriori algorithm, and it is almost as fast as FP-growth, but it needs smaller memory.