The Optimization Algorithm Of Association Rules Mining

被引：0

作者：

Liu, Zhenyu ^{[1
]}

Song, Zhihui ^{[1
]}

Yan, Ruiqing ^{[1
]}

Zhang, Zeng ^{[1
]}

机构：

[1] Univ Inner Mongolia, Dept Transportat Engn, Hohhot, Peoples R China

来源：

INTERNATIONAL CONFERENCE MACHINERY, ELECTRONICS AND CONTROL SIMULATION | 2014年 / 614卷

关键词：

Aprior; Frequent Itemsets; Association Rules; Subset-Tree;

D O I：

10.4028/www.scientific.net/AMM.614.405

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Frequent itemsets mining is the core part of association rule mining. At present most of the research on association rules mining is focused on how to improve the efficiency of mining frequent itemsets, however, the rule sets generated from frequent itemsets are the final results presented to decision makers for making, so how to optimize the rulesets generation process and the final rules is also worthy of attention. Based on encoding the dataset, this paper proposes a encoding method to speed up the generation process of frequent itemsets and proposes a subset tree to generate association rules which can simplify the generation process of rules and narrow the rulesets presented to decision makers.

引用

页码：405 / 408

页数：4