Research And Application Of Association Rules Mining Based On FP-growth Algorithm

Mining association rules from large datasets, which is one of the most important research fields in data mining, can reveal the interesting relationships between itemsets, therefore is widely applied to many fields such as marketing and sales, medicine, finance, biology, telecommunications, agriculture. Since 1993 R.Agrawal and R.Srikant firstly proposed the concept of association rules, a lot of algorithms have been developed for mining association rules.The most classic association rule mining algorithms are The Apriori algorithm and The FP-growth algorithm.FP-growth algorithm is one of the currently most popular algorithms for association rule mining without candidate generation. However, FP-growth algorithm can not mine effectively the large databases, moreover, the Time-complexity and the space-complexity is to high. To overcome these drawbacks, this paper proposed the DCFPmine algorithm which improved on FP-growth Algorithm. First, The DCFPmine algorithm adopt the technology of dividing the large database into many sub-database, it can overcome the question of effective mine large databases for the FP-growth algorithm. Second, The DCFPmine algorithm adopt the constrained-tree technology on mining prcedure, which can improve efficiency on the Time-complexity and the space-complexity. Experimental results show that The DCFPmine algorithm are superior to the FP-growth algorithm both in time and space efficiency.