© 2001

Data Mining for Scientific and Engineering Applications

  • Robert L. Grossman
  • Chandrika Kamath
  • Philip Kegelmeyer
  • Vipin Kumar
  • Raju R. Namburu

Part of the Massive Computing book series (MACO, volume 2)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Chandrika Kamath
    Pages 1-21
  3. Michael C. Burl
    Pages 63-84
  4. Roberta M. Humphreys, Juan E. Cabanela, Jeffrey Kriessler
    Pages 85-94
  5. Chandrika Kamath, Erick Cantú-Paz, Imola K. Fodor, Nu Ai Tang
    Pages 95-114
  6. Robert Grossman, Emory Creel, Marco Mazzucco, Roy Williams
    Pages 115-123
  7. Naren Ramakrishnan, Ananth Y. Grama
    Pages 125-139
  8. Mohammed J. Zaki, Chris Bystroff
    Pages 141-164
  9. Ruixin Yang, Menas Kafatos, Kwang-Su Yang, X. Sean Wang
    Pages 183-199
  10. I. Marusic, G. V. Candler, V. Interrante, P. K. Subbareddy, A. Moss
    Pages 223-238
  11. Eui-Hong Han, George Karypis, Vipin Kumar
    Pages 239-256
  12. Raghu Machiraju, James E. Fowler, David Thompson, Bharat Soni, Will Schroeder
    Pages 257-279
  13. Hillol Kargupta, Krishnamoorthy Sivakumar, Weiyun Huang, Rajeev Ayyagari, Rong Chen, Byung-Hoon Park et al.
    Pages 281-306
  14. Raj Bhatnagar
    Pages 307-317
  15. William M. Pottenger, Yong-Bin Kim, Daryl D. Meling
    Pages 319-333
  16. Harsha Nagesh, Sanjay Goil, Alok Choudhary
    Pages 335-356

About this book


Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications.
Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.


algorithms bioinformatics classification clustering computer science data analysis data mining database databases information network neural networks research Service statistics

Editors and affiliations

  • Robert L. Grossman
    • 1
  • Chandrika Kamath
    • 2
  • Philip Kegelmeyer
    • 3
  • Vipin Kumar
    • 4
  • Raju R. Namburu
    • 5
  1. 1.University of IllinoisChicagoUSA
  2. 2.Lawrence Livermore National LaboratoryLivermoreUSA
  3. 3.Sandia National LaboratoriesLivermoreUSA
  4. 4.Army High Performance Computing Research Center (AHPCRC)MinneapolisUSA
  5. 5.Army Research Laboratory, Aberdeen Proving GroundUSA

Bibliographic information

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