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Integration of Variable Precision Rough Set and Fuzzy Clustering: An Application to Knowledge Acquisition for Manufacturing Process Planning

  • Zhonghao Wang
  • Xinyu Shao
  • Guojun Zhang
  • Haiping Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3642)

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.

Keywords

Association Rule Knowledge Acquisition Fuzzy Cluster Fuzzy Approximation Variable Precision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bi, Y.X., Anderson, T., McClean, S.: A rough set model with ontology for discovering maximal association rules in documents collections. Knowledge-based Systems 16, 243–251 (2003)CrossRefGoogle Scholar
  2. 2.
    Bodjanova, S.: Approximation of fuzzy concepts in decision making. fuzzy sets and systems 85, 23–29 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Twofold fuzzy sets and rough sets-some issues in knowledge representation. Fuzzy Sets and Systems 23, 3–18 (1987)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Jagielska, I., Mattheews, C.: An investigation into the application of neural networks, fuzzy logic, genetic algorithms and rough sets to automated knowledge acquisition for classification problems. Neurocomputing 24, 37–54 (1999)zbMATHCrossRefGoogle Scholar
  5. 5.
    Lee, J.H.: Artificial intelligence-based sampling planning system for dynamic manufacturing process. Expert systems with application 22, 117–133 (2002)CrossRefGoogle Scholar
  6. 6.
    Ohashia, T., Motomura, M.: Expert system of cold forging defects using risk analysis tree network with fuzzy language. Journal of Materials Processing Technology 107, 260–266 (2000)CrossRefGoogle Scholar
  7. 7.
    Ong, S.K., Vin, L.J., Nee, A.Y.C., Kals, H.J.J.: Fuzzy set theory applied to bend sequencing for sheet metal bending. Journal of Materials Processing Technology 69, 29–36 (1997)CrossRefGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48–57 (1997)zbMATHCrossRefGoogle Scholar
  9. 9.
    Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data, MA. Kluwer Academic Publishers, Boston (1991)zbMATHGoogle Scholar
  10. 10.
    Pawlak, Z.: Rough set. International Journal of Computer and Information Science 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Shao, X.Y., Zhang, G.J., Li, P.G., Chen, Y.B.: Application of ID3 algorithm in knowledge acquisition for tolerance design. Journal of Materials Processing Technology 117, 66–74 (2001)CrossRefGoogle Scholar
  12. 12.
    Wu, W.Z., Mi, J.S., Zhang, W.X.: Generalized fuzzy rough set. Information sciences 151, 263–282 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Ziarko, W.: Variable precision rough sets model. Journal of Computer and Systems Sciences 46(1), 39–59 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Ziarko, W., Fei, X.: VPRSM Approach for web searching. In: RSFDGrC, pp. 514–521 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhonghao Wang
    • 1
  • Xinyu Shao
    • 1
  • Guojun Zhang
    • 1
  • Haiping Zhu
    • 1
  1. 1.School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanThe People’s Republic of China

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