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Data Mining for Scientific and Engineering Applications : Massive Computing - Robert L. Grossman

Data Mining for Scientific and Engineering Applications

Massive Computing

By: Robert L. Grossman (Editor), Chandrika Kamath (Editor), P. Kegelmeyer (Editor), V. Kumar (Editor), R. Namburu (Editor)

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Published: 31st October 2001
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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.

Foreword
List of Contributors
List of Reviewers
Preface
On Mining Scientific Datasetsp. 1
Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunitiesp. 23
Data Mining at the Interface of Computer Science and Statisticsp. 35
Mining Large Image Collectionsp. 63
Mining Astronomical Databasesp. 85
Searching for Bent-Double Galaxies in the First Surveyp. 95
A Dataspace Infrastructure for Astronomical Datap. 115
Data Mining Applications in Bioinformaticsp. 125
Mining Residue Contacts in Proteinsp. 141
KDD Services at the Gooddard Earth Sciences Distributed Archive Centerp. 165
Data Mining in Integrated Data Access and Data Analysis Systemsp. 183
Spatial Data Mining for Classification, Visualisation and Interpretation with Artmap Neural Networkp. 201
Real Time Feature Extraction for the Analysis of Turbulent Flowsp. 223
Data Mining for Turbulent Flowsp. 239
Evita-Efficient Visualization and Interrogation of Tera-Scale Datap. 257
Towards Ubiquitous Mining of Distributed Datap. 281
Decomposable Algorithms for Data Miningp. 307
HDDI: Hierarchical Distributed Dynamic Indexingp. 319
Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasetsp. 335
Efficient Clustering of Very Large Document Collectionsp. 357
A Scalable Hierarchical Algorithm for Unsupervised Clusteringp. 383
High-Performance Singular Value Decompositionp. 401
Mining High-Dimensional Scientific Data Sets Using Singular Value Decompositionp. 425
Spatial Dependence in Data Miningp. 439
Sparc: Spatial Association Rule-Based Classificationp. 461
What's Spatial About Spatial Data Mining: Three Case Studiesp. 487
Predicting Failures in Event Sequencesp. 515
Efficient Algorithms for Mining Long Patterns in Scientific Data Setsp. 541
Probabilistic Estimation in Data Miningp. 567
Classification Using Association Rules: Weaknesses and Enhancementsp. 591
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9781402000331
ISBN-10: 1402000332
Series: Massive Computing
Audience: General
Format: Hardcover
Language: English
Number Of Pages: 605
Published: 31st October 2001
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 24.4 x 17.0  x 3.81
Weight (kg): 2.33