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Data mining for design and manufacturing: methods and applications

Data mining for design and manufacturing: methods and applications (2회 대출)

Braha, Dan.
서명 / 저자사항
Data mining for design and manufacturing: methods and applications / edited by Dan Braha.
Dordrecht ;   Boston :   Kluwer Academic ,   c2001.  
xviii, 524 p. : ill. ; 25 cm.
Massive computing ; 3
1402000340 (alk. paper)
Includes bibliographical references and index.
Data mining. Design, Industrial. Manufacturing processes.
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245 0 0 ▼a Data mining for design and manufacturing: ▼b methods and applications / ▼c edited by Dan Braha.
260 ▼a Dordrecht ; ▼a Boston : ▼b Kluwer Academic , ▼c c2001.
300 ▼a xviii, 524 p. : ▼b ill. ; ▼c 25 cm.
490 1 ▼a Massive computing ; ▼v 3
504 ▼a Includes bibliographical references and index.
650 0 ▼a Data mining.
650 0 ▼a Design, Industrial.
650 0 ▼a Manufacturing processes.
700 1 ▼a Braha, Dan.
830 0 ▼a Massive computing ; ▼v v. 3.


No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 세종학술정보원/과학기술실/ 청구기호 006.3 D2322 등록번호 151130055 도서상태 대출가능 반납예정일 예약 서비스 C



Preface. Data Mining for Design and Manufacturing; D. Braha. Part I: Overview of Data Mining. 1. Data Mining: An Introduction; I.K. Sethi. 2. A Survey of Methodologies and Techniques for Data Mining and Intelligent Data Discovery; R. Gonzalez, A. Kamrani. Part II: Data Mining in Product Design. 3. Data Mining in Scientific Data; S. Rudolph, P. Hertkorn. 4. Learning to Set Up Numerical Optimizations of Engineering Designs; M. Schwabacher, et al. 5. Automatic Classification and Creation of Classification Systems Using Methodologies of `Knowledge Discovery in Databases (KDD)'; H. Grabowski, et al. 6. Data Mining for Knowledge Acquisition in Engineering Design; Y. Ishino, Y. Jin. 7. A Data Mining-Based Engineering Design Support Systema: A Research Agenda; C.J. Romanowski, R. Nagi. Part III: Data Mining in Manufacturing. 8. Data Mining for High Quality and Quick Response Manufacturing; J.-H. Lee, S.-C. Park. 9. Data Mining for Process and Quality Control in the Semiconductor Industry; M. Last, A. Kandel. 10. Analyzing Maintenance Dance Using Data Mining Methods; C.J. Romanowski, R. Nagi. 11. Methodology of Mining Massive Data Sets for Improving Manufacturing Quality/Efficiency; J.-C. (JC) Lu. 12. Intelligent Process Control System for Quality Improvement by Data Mining in the Process Industry; S. Oh, et al. 13. Data Mining by Attribute Decompositon with Semiconductor Manufacturing Case Study; O. Maimon, L.S. Rokach. 14. Derivation of Decision Rules for the Evaluation ofProduct Performance Using Genetic Algorithms and Rough Set Theory; Z. Lian-Yin, et al. 15. An Evaluation of Sampling Methods for Data Mining with Fuzzy C-Means; K. Josien, et al. 16. Colour Space Mining for Industrial Monitoring; K.J. Brazier, et al. 17. Non-Traditional Applications of Data Mining; A. Kusiak. 18. Fuzzy-Neural-Genetic Layered Multi-Agent Reactive Control of Robotic Soccer; A.V. Topalov, S.G. Tzafestas. Part IV: Enabling Technologies for Data Mining in Design and Manufacturing. 19. Method-Specific Knowledge Compilation; J.W. Murdock, et al. 20. A Study of Technical Challenges in Relocation of a Manufacturing Site; G. Zhang, S. Athalye. 21. Using Imprecise Analogical Reasoning to Refine the Query Answers for Heterogeneous Multidatabase Systems in Virtual Enterprises; Z.M. Ma, et al. 22. The Use of Process Capability Data in Design; A. Thornton. Index.

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