Data Mining for Design and Manufacturing

1. Front Matter

   Pages i-xviii

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2. Overview of Data Mining

   1. Data Mining: An Introduction

      • Ishwar K. Sethi

      Pages 1-40

   2. A Survey of Methodologies and Techniques for Data Mining and Intelligent Data Discovery

      • Ricardo Gonzalez, Ali Kamrani

      Pages 41-59

3. Data Mining in Product Design

   1. Data Mining in Scientific Data

      • Stephan Rudolph, Peter Hertkorn

      Pages 61-85

   2. Learning to Set Up Numerical Optimizations of Engineering Designs

      • Mark Schwabacher, Thomas Ellman, Haym Hirsh

      Pages 87-125

   3. Automatic Classification and Creation of Classification Systems Using Methodologies of “Knowledge Discovery in Databases (KDD)”

      • Hans Grabowski, Ralf-Stefan Lossack, Jörg Weißkopf

      Pages 127-143

   4. Data Mining for Knowledge Acquisition in Engineering Design

      • Yoko Ishino, Yan Jin

      Pages 145-160

   5. A Data Mining-Based Engineering Design Support System: A Research Agenda

      • Carol J. Romanowski, Rakesh Nagi

      Pages 161-178

4. Data Mining in Manufacturing

   1. Data Mining for High Quality and Quick Response Manufacturing

      • Jang-Hee Lee, Sang-Chan Park

      Pages 179-205

   2. Data Mining for Process and Quality Control in the Semiconductor Industry

      • Mark Last, Abraham Kandel

      Pages 207-234

   3. Analyzing Maintenance Data Using Data Mining Methods

      • Carol J. Romanowski, Rakesh Nagi

      Pages 235-254

   4. Methodology of Mining Massive Data Sets for Improving Manufacturing Quality/Efficiency

      • Jye-Chyi Lu

      Pages 255-288

   5. Intelligent Process Control System for Quality Improvement by Data Mining in the Process Industry

      • Sewon Oh, Jooyung Han, Hyunbo Cho

      Pages 289-309

   6. Data Mining by Attribute Decomposition with Semiconductor Manufacturing Case Study

      • Oded Maimon, Lior S. Rokach

      Pages 311-336

   7. Derivation of Decision Rules for the Evaluation of Product Performance Using Genetic Algorithms and Rough Set Theory

      • Zhai Lian-Yin, Khoo Li-Pheng, Fok Sai-Cheong

      Pages 337-353

   8. An Evaluation of Sampling Methods for Data Mining with Fuzzy C-Means

      • K. Josien, G. Wang, T. W. Liao, E. Triantaphyllou, M. C. Liu

      Pages 355-369

   9. Colour Space Mining For Industrial Monitoring

      • K. J. Brazier, A. G. Deakin, R. D. Cooke, P. C. Russell, G. R. Jones

      Pages 371-400

   10. Non-Traditional Applications of Data Mining

       • Andrew Kusiak

       Pages 401-416

   11. Fuzzy-Neural-Genetic Layered Multi-Agent Reactive Control of Robotic Soccer

       • Andon V. Topalov, Spyros G. Tzafestas

       Pages 417-442

5. Enabling Technologies for Data Mining in Design and Manufacturing

   1. Method-Specific Knowledge Compilation

      • J. William Murdock, Ashok K. Goel, Michael J. Donahoo, Shamkant Navathe

      Pages 443-463

Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making.
Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.

• DES
• classification
• data mining
• genetic algorithm
• information
• knowledge
• knowledge discovery
• learning
• optimization
• problem solving
• proving
• robot

• Ben-Gurion University, Israel

  Dan Braha

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