Abstract：Association rules are a concept in data mining.The association between data is found by analyzing the data.Massive data will generate a large number of redundant and similar association rules,affecting users' understanding and judgment of the rules.This article uses the iris data set for experiments.First,three test indexes are established to delete redundant association rules.When performing K-means analysis,the triangles generated by the rules are used to iteratively select the initial point,and then the redundant rules are clustered.The experiments confirm that the method in this paper classifies similar association rules into a cluster,which can effectively help users quickly find useful association rules,help users better understand and analyze the rules,and improve the efficiency of clustering.
李珺,刘鹤,朱良宽. 基于改进的K-means算法的关联规则数据挖掘研究[J]. 小型微型计算机系统, 2021, 42(1): 15-19.
LI Jun,LIU He,ZHU Liang-kuan. Research on Association Rule Data Mining Based on Improved K-means Algorithm. Journal of Chinese Computer Systems, 2021, 42(1): 15-19.
2021, Vol. 42 
