An efficient parallel association rules mining algorithm for fault diagnosis

作     者：Ji, Haipeng Wang, Taiyong Liu, Jing Fan, Shiyan Wang, Zhipeng Zhang, Kairan 

作者机构：Mechanical Engineering College Tianjin University Tianjin China Mechanical Engineering College Yanshan University Qinhuangdao Hebei China School of Computer Science and Engineering Hebei University of Technology Tianjin China Hebei Key Laboratory of Dig Data Calculation Hebei University of Technology Tianjin China School of Economics and Management Hebei University of Technology Tianjin China 

出 版 物：《Key Engineering Materials》 (Key Eng Mat)

年 卷 期：2016年第693卷

页      面：1326-1330页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by 2014 Hebei Province Natural Science Fund (No.G2014202031) and 2013 Hebei Provincial Department of education science research program (QN20131060) 

主　　题：Data mining 

摘      要：With the development of Internet industry, equipment data is increasing. The traditional method is not suitable for processing large data. Aiming at inefficient problem of Apriori algorithm when mining very large database, an efficient parallel association rules mining algorithm (Advanced Pruning Parallel Apriori Algorithm) based on a cluster is presented. APPAA algorithm can enhance the mining efficiency, as well as the system s extension. Experimental results show that APPAA algorithm cuts down 85% mining time of Apriori, and it has good characteristics of parallel and *** it is suitable for mining very large size database of fault diagnosis. © 2016 Trans Tech Publications, Switzerland.