Research On Novel Model of Data Mining Based on Improved Association Rules and Clustering Algorithm

被引：0

作者：

Tan, Qing ^{[1
]}

机构：

[1] Luoyang Normal Univ, Coll Informat Technol, Luoyang 471934, Henan, Peoples R China

来源：

PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT, COMPUTER AND SOCIETY (EMCS 2017) | 2017年 / 61卷

关键词：

Apriori algorithm; Decision tree; Association rule; Clustering; Data mining; EFFICIENT METHOD;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Apriori algorithm is one of the most effective algorithms for mining frequent itemsets of Boolean Association rules. Decision tree is a method to analyze and summarize the attributes of a large number of samples. The frequent itemsets are used to generate the association rules, and the strong association rules are generated according to the minimum confidence set by the user. The paper presents research on novel model of data mining based on improved association rules and clustering algorithm. Finally, the effectiveness of the proposed algorithm is verified by experiments.

引用

页码：522 / 526

页数：5

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