Title :
An improved apriori algorithm for early warning of equipment failue
Author :
Jing, Liu ; Yongquan, Lu ; Jintao, Wang ; Pengdong, Gao ; Chu, Qiu ; Haipeng, Ji ; Nan, Li ; Wenhua, Yu
Author_Institution :
High Performance Comput. Center, Commun. Univ. of China, Beijing, China
Abstract :
With large database, the process of mining association rules is time consuming. The efficiency becomes crucial factor. By analyzing Apriori algorithm and its improvement, the improved Apriori algorithm is applied to early warning of equipment failure. Moreover, Apriori algorithm is improved by reducing the number of scanning data base and the number of candidate item-set in advance which might become frequent item. Apriori algorithm and the improved Apriori algorithm are compared by the example of equipment failure. Finally, the improved Apriori algorithm is proved that it can improve the efficiency by experiment.
Keywords :
data mining; Apriori algorithm; association rules; database; early warning; equipment failue; Arithmetic; Association rules; Data mining; Databases; Educational institutions; Electronic mail; Equipment failure; Failure analysis; High performance computing; Mechanical engineering; Apriori algorithm; association rules mining(ARM); early warning of equipment failure;
Conference_Titel :
Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4519-6
Electronic_ISBN :
978-1-4244-4520-2
DOI :
10.1109/ICCSIT.2009.5234681
