Comparison and improvement of association rule mining algorithm
Xiao-Feng Gu, Xiao-Juan Hou, Chen-Xi Ma, Ao-Guang Wang, Hui-Ben Zhang, Xiaohua Wu, Xiao-Ming Wang · 2015
In recent years, the data mining technology has been developed rapidly. New efficient algorithms are emerging. Association data mining plays an important role in data mining, and the frequent item sets are the highest and the most costly. This paper is based on the association rules data mining technology. The advantages and disadvantages of Apriori algorithm and FP-growth algorithm are deeply analyzed in the association rules, and a new algorithm is proposed, finally, the performance of the algorithm is compared with the experimental results. It provides a reference for the extension and improvement of the algorithm of association rule mining.