Logo - springer
Slogan - springer

Computer Science - Security and Cryptology | Data Mining for Scientific and Engineering Applications

Data Mining for Scientific and Engineering Applications

Series: Massive Computing, Vol. 2

Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R. (Eds.)

2001, XX, 605 p.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$159.00

(net) price for USA

ISBN 978-1-4615-1733-7

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$359.00

(net) price for USA

ISBN 978-1-4020-0033-1

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$199.00

(net) price for USA

ISBN 978-1-4020-0114-7

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

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.

Content Level » Research

Related subjects » Artificial Intelligence - Engineering - Physical & Information Science - Security and Cryptology - Theoretical Computer Science

Table of contents 

Foreword. List of Contributors. List of Reviewers. Preface. 1. On Mining Scientific Datasets; C. Kamath. 2. Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunities; J. Chandra. 3. Data Mining at the Interface of Computer Science and Statistics; P. Smyth. 4. Mining Large Image Collections; M.C. Burl. 5. Mining Astronomical Databases; R.M. Humphreys, et al. 6. Searching for Bent-Double Galaxies in the First Survey; C. Kamath, et al. 7. A Dataspace Infrastructure for Astronomical Data; R. Grossman, et al. 8. Data Mining Applications in Bioinformatics; N. Ramakrishnan, A.Y. Grama. 9. Mining Residue Contacts in Proteins; M.J. Zaki, C. Bystroff. 10. KDD Services at the Goodard Earth Sciences Distributed Archive Center; C. Lynnes, R. Mack. 11. Data Mining in Integrated Data Access and Data Analysis Systems; R. Yang, et al. 12. Spatial Data Mining for Classification, Visualisation and Interpretation with Artmap Neural Network; W. Liu, et al. 13. Real Time Feature Extraction for the Analysis of Turbulent Flows; I. Marusic, et al. 14. Data Mining for Turbulent Flows; E.-H. Han, et al. 15. Evita-Efficient Visualization and Interrogation of Tera-Scale Data; R. Machiraju, et al. 16. Towards Ubiquitous Mining of Distributed Data; H. Kargupta, et al. 17. Decomposable Algorithms for Data Mining; R. Bhatnagar. 18. HDDI®: Hierarchical Distributed Dynamic Indexing; W.M. Pottenger, et al.19. Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets; H. Nagesh, et al. 20. Efficient Clustering of Very Large Document Collections; I.S. Dhillon, et al. 21. A Scalable Hierarchical Algorithm for Unsupervised Clustering; D. Boley. 22. High-Performance Singular Value Decomposition; D.B. Skillicorn, X. Yang. 23. Mining High-Dimensional Scientific Data Sets Using Singular Value Decomposition; E. Maltseva, et al. 24. Spatial Dependence in Data Mining; J.P. LeSage, R.K. Pace. 25. Sparc: Spatial Association Rule-Based Classification; J. Han, et al. 26. What's Spatial About Spatial Data Mining: Three Case Studies; S. Shekhar, et al. 27. Predicting Failures in Event Sequences; M.J. Zaki, et al. 28. Efficient Algorithms for Mining Long Patterns in Scientific Data Sets; R.C. Agarwal, C.C. Aggarwal. 29. Probabilistic Estimation in Data Mining; E.P.D. Pednault, C. Apte. 30. Classification Using Association Rules: Weaknesses and Enhancements; B. Liu, et al.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Data Structures, Cryptology and Information Theory.