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Data Mining for Scientific and Engineering Applications

  • Book
  • © 2001

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Editors:
  1. Robert L. Grossman

    1. University of Illinois, Chicago, USA

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  2. Chandrika Kamath

    1. Lawrence Livermore National Laboratory, Livermore, USA

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  3. Philip Kegelmeyer

    1. Sandia National Laboratories, Livermore, USA

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  4. Vipin Kumar

    1. Army High Performance Computing Research Center (AHPCRC), Minneapolis, USA

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  5. Raju R. Namburu

    1. Army Research Laboratory, Aberdeen Proving Ground, USA

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

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Table of contents (30 chapters)

  1. Front Matter

    Pages i-xx
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  2. On Mining Scientific Datasets

    • Chandrika Kamath
    Pages 1-21

  3. Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunities

    • Jagdish Chandra
    Pages 23-34

  4. Data Mining at the Interface of Computer Science and Statistics

    • Padhraic Smyth
    Pages 35-61

  5. Mining Large Image Collections

    • Michael C. Burl
    Pages 63-84

  6. Mining Astronomical Databases

    • Roberta M. Humphreys, Juan E. Cabanela, Jeffrey Kriessler
    Pages 85-94

  7. Searching for Bent-Double Galaxies in the First Survey

    • Chandrika Kamath, Erick Cantú-Paz, Imola K. Fodor, Nu Ai Tang
    Pages 95-114

  8. A Dataspace Infrastructure for Astronomical Data

    • Robert Grossman, Emory Creel, Marco Mazzucco, Roy Williams
    Pages 115-123

  9. Data Mining Applications in Bioinformatics

    • Naren Ramakrishnan, Ananth Y. Grama
    Pages 125-139

  10. Mining Residue Contacts in Proteins

    • Mohammed J. Zaki, Chris Bystroff
    Pages 141-164

  11. KDD Services at the Goddard Earth Sciences Distributed Active Archive Center

    • Christopher Lynnes, Robert Mack
    Pages 165-181

  12. Data Mining in Integrated Data Access and Data Analysis Systems

    • Ruixin Yang, Menas Kafatos, Kwang-Su Yang, X. Sean Wang
    Pages 183-199

  13. Spatial Data Mining for Classification, Visualisation and Interpretation with Artmap Neural Network

    • Weiguo Liu, Sucharita Gopal, Curtis Woodcock
    Pages 201-221

  14. Real Time Feature Extraction for the Analysis of Turbulent Flows

    • I. Marusic, G. V. Candler, V. Interrante, P. K. Subbareddy, A. Moss
    Pages 223-238

  15. Data Mining for Turbulent Flows

    • Eui-Hong Han, George Karypis, Vipin Kumar
    Pages 239-256

  16. EVITA — Efficient Visualization and Interrogation of Tera-Scale Data

    • Raghu Machiraju, James E. Fowler, David Thompson, Bharat Soni, Will Schroeder
    Pages 257-279

  17. Towards Ubiquitous Mining of Distributed Data

    • Hillol Kargupta, Krishnamoorthy Sivakumar, Weiyun Huang, Rajeev Ayyagari, Rong Chen, Byung-Hoon Park et al.
    Pages 281-306

  18. Decomposable Algorithms for Data Mining

    • Raj Bhatnagar
    Pages 307-317

  19. HDDI™: Hierarchical Distributed Dynamic Indexing

    • William M. Pottenger, Yong-Bin Kim, Daryl D. Meling
    Pages 319-333

  20. Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets

    • Harsha Nagesh, Sanjay Goil, Alok Choudhary
    Pages 335-356
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Keywords

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.

Editors and Affiliations

  • University of Illinois, Chicago, USA

    Robert L. Grossman

  • Lawrence Livermore National Laboratory, Livermore, USA

    Chandrika Kamath

  • Sandia National Laboratories, Livermore, USA

    Philip Kegelmeyer

  • Army High Performance Computing Research Center (AHPCRC), Minneapolis, USA

    Vipin Kumar

  • Army Research Laboratory, Aberdeen Proving Ground, USA

    Raju R. Namburu

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