Research on the FP Growth Algorithm about Association Rule Mining

Abstract

For large databases, the research on improving the mining performance and precision is necessary, so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. Generally all the frequent item sets discovery from the database in the process of association rule mining shares of larger, the price is also spending more. This paper introduces an improved aprior algorithm so called FP-growth algorithm that will help resolve two neck-bottle problems of traditional apriori algorithm and has more efficiency than original one. In theoretic research, An anatomy of two representative arithmetics of the Apriori and the FP Growth explains the mining process of frequent patterns item set. The constructing method of FP tree structure is provided and how it affects association rule mining is discussed. Experimental results show that the algorithm has higher mining efficiency in execution time, memory usage and CPU utilization than most current ones like Apriori.