An Efficient Parallel Association Rules Mining Algorithm for Fault Diagnosis

• Published in: Key Engineering Materials Vol. 693; pp. 1326 - 1330
• Main Authors: Tai Yong, Wang, Zhang, Kai Ran, Fan, Shi Yan, Ji, Hai Peng, Wang, Zhi Peng, Liu, Jing
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.05.2016
• Subjects: Algorithms; Clusters; Data mining; Fault diagnosis; Industrial development; Internet; Pruning; Data Mining; Fault Diagnosis; Apriori Algorithm; Parallel Computing
• ISBN: 3038357138; 9783038357131
• ISSN: 1013-9826; 1662-9795; 1662-9795
• DOI: 10.4028/www.scientific.net/KEM.693.1326

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 expandable.so it is suitable for mining very large size database of fault diagnosis.