An Efficient Parallel Association Rules Mining Algorithm for Fault Diagnosis

Hai Peng Ji; Wang Tai Yong; Jing Liu; Shi Yan Fan; Zhi Peng Wang; Kai Ran Zhang

doi:10.4028/www.scientific.net/KEM.693.1326

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 693An Efficient Parallel Association Rules Mining...

An Efficient Parallel Association Rules Mining Algorithm for Fault Diagnosis

Abstract:

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.

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Periodical:

Key Engineering Materials (Volume 693)

Pages:

1326-1330

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.693.1326

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Online since:

May 2016

Authors:

Hai Peng Ji, Wang Tai Yong*, Jing Liu, Shi Yan Fan, Zhi Peng Wang, Kai Ran Zhang

Keywords:

Apriori Algorithm, Data Mining, Fault Diagnosis, Parallel Computing

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