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Data Mining for Design and Manufacturing

Methods and Applications

  • Dan Braha

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

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Overview of Data Mining

  3. Data Mining in Product Design

    1. Stephan Rudolph, Peter Hertkorn
      Pages 61-85
    2. Mark Schwabacher, Thomas Ellman, Haym Hirsh
      Pages 87-125
    3. Carol J. Romanowski, Rakesh Nagi
      Pages 161-178
  4. Data Mining in Manufacturing

    1. Jang-Hee Lee, Sang-Chan Park
      Pages 179-205
    2. Carol J. Romanowski, Rakesh Nagi
      Pages 235-254
    3. K. Josien, G. Wang, T. W. Liao, E. Triantaphyllou, M. C. Liu
      Pages 355-369
    4. K. J. Brazier, A. G. Deakin, R. D. Cooke, P. C. Russell, G. R. Jones
      Pages 371-400
    5. Andrew Kusiak
      Pages 401-416
    6. Andon V. Topalov, Spyros G. Tzafestas
      Pages 417-442
  5. Enabling Technologies for Data Mining in Design and Manufacturing

    1. J. William Murdock, Ashok K. Goel, Michael J. Donahoo, Shamkant Navathe
      Pages 443-463
    2. Guangming Zhang, Sameer Athalye
      Pages 465-486
    3. Anna Thornton
      Pages 505-518
  6. Back Matter
    Pages 519-524

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  • Dan Braha
    • 1
  1. 1.Ben-Gurion UniversityIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-4911-3
  • Copyright Information Springer Science+Business Media Dordrecht 2001
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-5205-9
  • Online ISBN 978-1-4757-4911-3
  • Series Print ISSN 1569-2698
  • Series Online ISSN 2468-8738
  • Buy this book on publisher's site
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