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Data Mining for Design and Manufacturing

Methods and Applications

  • Book
  • © 2001

Overview

Editors:
  1. Dan Braha

    1. Ben-Gurion University, Israel

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Part of the book series: Massive Computing (MACO, volume 3)

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Table of contents (22 chapters)

  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
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Keywords

About this book

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.

Editors and Affiliations

  • Ben-Gurion University, Israel

    Dan Braha

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