摘要： 讨论粒计算在关联规则挖掘中的应用，通过对基本信息粒的划分、对粒子对象集合的映射，减少扫描项集所在的对象集合，提高算法的运行效率，从而更好地处理海量数据的规则发现，更适用于支持度较小、复杂度较高的数据集。仿真试验证明该算法有较低的求解复杂度及较高的求解效率。

关键词: 粒计算, 关联规则, 频繁项集

Abstract: This paper discusses the application of granular computing in association rules mining. Through partition of information granules and map of granule object sets, the algorithm reduces the object sets required when scanning datasets, and improves the running efficiency, so that it can deal with the association rules discovery of massive dataset better, and it is more suitable for the dataset with small support and high complexity. Experimental results show that the algorithm has low computing complexity and high efficiency.

Key words: granular computing, association rules, frequent itemsets

中图分类号: 

• TP181