An Improved Parallel Apriori Algorithm and its Appliance to a Coal Production Scheduling Monitor System

被引：0
|
作者：
Wu, Min [1 ]
Yin, Shi-Qun [1 ]
Han, Zhan-Hao [1 ]
机构：
[1] Southwest Univ, Fac Comp & Informat Sci, Chongqing, Peoples R China
来源：
FUZZY SYSTEM AND DATA MINING | 2016年 / 281卷
关键词：
Parallel algorithm; Frequent item sets; MapReduce; production scheduling;
D O I：
10.3233/978-1-61499-619-4-436
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper discusses the massive data processing method-Apriori algorithm for mining association rules. The Apriori algorithm repeatedly scans the database for shortcomings. Based on a Hadoop platform to block huge amounts of data, we provide the massive data processing method-Apriori algorithm for mining association rules.
引用
收藏
页码：436 / 441
页数：6
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