Abstract
Knowledge acquisition plays a significant role in the knowledge-based intelligent process planning system, but there remains a difficult issue. In manufacturing process planning, experts often make decisions based on different decision thresholds under uncertainty. Knowledge acquisition has been inclined towards a more complex but more necessary strategy to obtain such thresholds, including confidence, rule strength and decision precision. In this paper, a novel approach to integrating fuzzy clustering and VPRS (variable precision rough set) is proposed. As compared to the conventional fuzzy decision techniques and entropy-based analysis method, it can discover association rules more effectively and practically in process planning with such thresholds. Finally, the proposed approach is validated by the illustrative complexity analysis of manufacturing parts, and the analysis results of the preliminary tests are also reported.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, Z., Shao, X., Zhang, G., Zhu, H. (2005). Integration of Variable Precision Rough Set and Fuzzy Clustering: An Application to Knowledge Acquisition for Manufacturing Process Planning. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_62
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DOI: https://doi.org/10.1007/11548706_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
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