Research and application of multi-association rule mining algorithm
Min Cao · Jisuanji gongcheng yu sheji · 2010
Aiming at the problem of a large number of transactions in a banking business systems,an improved multilevel association rules algorithm based on the FP-growth algorithm of data mining which improves the frequent itemsets of the data mining is put forward.By analyzing the massive data of the current operational system,the algorithm is classfied based on division of profits.The size of the tree is significantly reduced,and on the basis of the tree,a maximal target frequent itemset mining algorithm is put forward,which satisfies the users requirements and accelerates the speed to traverse the tree,so the mining efficiency is improved in the algorithm.A simulation example is presented to prove the proposed method.Consequently,the proposed algorithm can better adapt to the hierarchical structure of the commercial banking system in large-scale data sets data mining.