Mining temporal association rules with frequent itemsets tree

被引：36
|
作者：
Wang, Ling [1 ,2 ]
Meng, Jianyao [1 ,2 ]
Xu, Peipei [1 ,2 ]
Peng, Kaixiang [1 ,2 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[2] Minist Educ, Key Lab Adv Control Iron & Steel Proc, Beijing 100083, Peoples R China
来源：
APPLIED SOFT COMPUTING | 2018年 / 62卷
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Temporal relationship; Frequent itemsets tree; Temporal association rule; Interpretability;
D O I：
10.1016/j.asoc.2017.09.013
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel framework for mining temporal association rules by discovering itemsets with frequent itemsets tree is introduced. In order to solve the problem of handling time series by including temporal relation between the multi items into association rules, a frequent itemsets tree is constructed in parallel with mining frequent itemsets to improve the efficiency and interpretability of rule mining without generating candidate itemsets. Experimental results show that our algorithm can provide better efficiency and interpretability in mining temporal association rules in comparison with other algorithms and has good application prospects. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：817 / 829
页数：13
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