Data Mining for Scientific and Engineering Applications

By: R.L. Grossman (Editor), C. Kamath (Editor), P. Kegelmeyer (Editor)

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Paperback | 31 October 2001

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628 Pages

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23.5 x 15.88 x 3.18

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Medicine Medicine in General Computer Science Image Processing Computer Programming & Software Development Algorithms & Data Structures Artificial Intelligence Mathematics

Probability & Statistics Engineering & Technology Technology in General Engineering in General

Foreword
List of Contributors
List of Reviewers
Preface
On Mining Scientific Datasets
Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunities
Data Mining at the Interface of Computer Science and Statistics
Mining Large Image Collections
Mining Astronomical Databases
Searching for Bent-Double Galaxies in the First Survey
A Dataspace Infrastructure for Astronomical Data
Data Mining Applications in Bioinformatics
Mining Residue Contacts in Proteins
KDD Services at the Goodard Earth Sciences Distributed Archive Center
Data Mining in Integrated Data Access and Data Analysis Systems
Spatial Data Mining for Classification
Visualisation and Interpretation with Artmap Neural Network
Real Time Feature Extraction for the Analysis of Turbulent Flows
Data Mining for Turbulent Flows
Evita-Efficient Visualization and Interrogation of Tera-Scale Data
Towards Ubiquitous Mining of Distributed Data
Decomposable Algorithms for Data Mining
HDDI½: Hierarchical Distributed Dynamic Indexing
Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets
Efficient Clustering of Very Large Document Collections
A Scalable Hierarchical Algorithm for Unsupervised Clustering
High-Performance Singular Value Decomposition
Mining High-Dimensional Scientific Data Sets Using Singular Value Decomposition
Spatial Dependence in Data Mining
Sparc: Spatial Association Rule-Based Classification
What's Spatial About Spatial Data Mining: Three Case Studies
Predicting Failures in Event Sequences
Efficient Algorithms for Mining Long Patterns in Scientific Data Sets
Probabilistic Estimation in Data Mining
Classification Using Association Rules: Weaknesses and Enhancements
Table of Contents provided by Publisher. All Rights Reserved.

Data Mining for Scientific and Engineering Applications

At a Glance

Paperback

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How to return your order

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