Data Mining for Scientific and Engineering Applications
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 tec...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
2001, 2001
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| Edition: | 1st ed. 2001 |
| Series: | Massive Computing
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1 On Mining Scientific Datasets
- 2 Understanding High Dimensional And Large Data Sets: Some Mathematical Challenges And Opportunities
- 3 Data Mining At The Interface of Computer Science and Statistics
- 4 Mining Large Image Collections
- 5 Mining Astronomical Databases
- 6 Searching for Bent-Double Galaxies in The First Survey
- 7 A Dataspace Infrastructure for Astronomical Data
- 8 Data Mining Applications in Bioinformatics
- 9 Mining Residue Contacts in Proteins
- 10 Kdd Services at The Goddard Earth Sciences Distributed Archive Center
- 11 Data Mining in Integrated Data Access and Data Analysis Systems
- 12 Spatial Data Mining For Classification, Visualisation And Interpretation With Artmap Neural Network
- 13 Real Time Feature Extraction for The Analysis of Turbulent Flows
- 14 Data Mining for Turbulent Flows
- 15 Evita-Efficient Visualization and Interrogation of Tera-Scale Data
- 16 Towards Ubiquitous Mining of Distributed Data
- 17 Decomposable Algorithms for Data Mining
- 18 HDDI™: Hierarchical Distributed Dynamic Indexing
- 19 Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets
- 20 Efficient Clustering of Very Large Document Collections
- 21 A Scalable Hierarchical Algorithm for Unsupervised Clustering
- 22 High-Performance Singular Value Decomposition
- 23 Mining High-Dimensional Scientific Data Sets Using Singular Value Decomposition
- 24 Spatial Dependence in Data Mining
- 25 Sparc: Spatial Association Rule-Based Classification
- 26 What’s Spatial about Spatial Data Mining: Three Case Studies
- 27 Predicting Failures in Event Sequences
- 28 Efficient Algorithms for Mining Long Patterns In Scientific Data Sets
- 29 Probabilistic Estimation in Data Mining
- 30 Classification Using Associationrules: Weaknesses And Enhancements