An Efficient Frequent Patterns Mining Algorithm Based on Apriori Algorithm and the FP-Tree Structure

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.