A Fast Algorithm for Mining Temporal Association Rules Based on a New Definition

被引：0
|
作者：
Zhan, Li [1 ]
Yu, Fusheng [1 ]
Zhang, Huixin [2 ]
机构：
[1] Beijing Normal Univ, Sch Math Sci, Minist Educ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
[2] Capital Univ Econ & Business, Sch Stat, Beijing 100070, Peoples R China
来源：
2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | 2017年
基金：
中国国家自然科学基金;
关键词：
Data mining; association rules; temporal association rules; support and confidence;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In literatures, there were several forms of definitions for temporal association rules which have different formulas for calculating support and confidence. In this paper, after reviewing the literatures, we first reform the definition of the association rule, and then give a general form of temporal association rule which was equipped with new formula for calculating its support and confidence. Based on the new definition, we propose a fast algorithm for mining temporal association rules. Experiments on a synthetic dataset and a real dataset exhibit the good performance of the new proposed algorithm.
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收藏
页数：6
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