Title :
ResHDrch of classification for defective components of automotive recall based on clustering algorithm
Author :
Lanxiang, Lian ; Li, Gao ; Zhihui, Cheng ; Chunsong, Hu
Author_Institution :
Beijing Inst. of Technol., Beijing, China
Abstract :
The classification of Recall defective components in China is unsuitable for further research and facial operation. Hierarchical clustering algorithm was present to cluster the classification of vehicle parts and components which will be used in vehicle defects recall. Based on QC/T 265-2004 named regulation of vehicle parts and components and American NHTSA recall data, 32 classes was clustered through digital processing of the attribute value of every dimension of data samples that can distinguish one component from the other. Obviously the clustered results are suitable for facial operation and reasonable, meanwhile they partly avoided from unreasonable classes.
Keywords :
automobile industry; automotive components; pattern classification; pattern clustering; production engineering computing; American NHTSA recall data; China; QC/T 265-2004; automotive recall; digital processing; hierarchical clustering algorithm; recall defective component classification; vehicle part classification; Automobiles; Classification algorithms; Clustering algorithms; History; Safety; Wheels; Automobile Recall; Hierarchical Clustering Algorithm; Vehicle Parts and Components;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968983
