Research on Data Mining Framework Based on Improved Sequential Association Rule Discovery

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 2016 6TH INTERNATIONAL CONFERENCE ON MECHATRONICS, COMPUTER AND EDUCATION INFORMATIONIZATION (MCEI 2016) | 2016年 / 130卷
关键词：
Sequential association rule; Data mining; Apriori algorithm; Clustering; FP tree;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper firstly analyzes the shortcomings of sequential association rule discovery technology, and proposes the improvement method to make up the deficiency. Then, the paper discusses the data mining method based on association rules. The paper presents research on data mining framework based on improved sequential association rule discovery. This novel method can make use of frequent itemsets to generate the required association rules, according to the user set the minimum credibility of the choice, the generation of time sequence association rules.
引用
收藏
页码：324 / 328
页数：5
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