by A. Schoch, R. Refflinghaus, N. Schmitzberger, A. Wolters
Abstract
Association rule mining (ARM) is a data mining technique that extracts relationships between variables in dataset. This paper presents an application of ARM for dynamic error classification in the automotive manufacturing industry. The method involves the extraction of frequent patterns from historical vehicle configuration data to build a rule-based system for error classification. The presented approach includes frequent item sets, rule mining and the estimation of conditional error probabilities. The results show that ARM is a promising approach for dynamic error classification in the automotive manufacturing industry and has potential to improve product quality.
Keywords: Automotive Industry; Association Rule Mining; Quality Management; Error Classification
Video presentation
Presenting author
 | Name: Affiliation: Email: | Alexander Wolters BMW Group, Data Lab, Germany quality.office@uni-kassel.de |
