Data Mining for Scientific and Engineering Applications (Massive Computing, 2)

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. Front Matter....Pages i-xx On Mining Scientific Datasets....Pages 1-21 Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunities....Pages 23-34 Data Mining at the Interface of Computer Science and Statistics....Pages 35-61 Mining Large Image Collections....Pages 63-84 Mining Astronomical Databases....Pages 85-94 Searching for Bent-Double Galaxies in the First Survey....Pages 95-114 A Dataspace Infrastructure for Astronomical Data....Pages 115-123 Data Mining Applications in Bioinformatics....Pages 125-139 Mining Residue Contacts in Proteins....Pages 141-164 KDD Services at the Goddard Earth Sciences Distributed Active Archive Center....Pages 165-181 Data Mining in Integrated Data Access and Data Analysis Systems....Pages 183-199 Spatial Data Mining for Classification, Visualisation and Interpretation with Artmap Neural Network....Pages 201-221 Real Time Feature Extraction for the Analysis of Turbulent Flows....Pages 223-238 Data Mining for Turbulent Flows....Pages 239-256 EVITA — Efficient Visualization and Interrogation of Tera-Scale Data....Pages 257-279 Towards Ubiquitous Mining of Distributed Data....Pages 281-306 Decomposable Algorithms for Data Mining....Pages 307-317 HDDITM: Hierarchical Distributed Dynamic Indexing....Pages 319-333 Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets....Pages 335-356 Efficient Clustering of Very Large Document Collections....Pages 357-381 A Scalable Hierarchical Algorithm for Unsupervised Clustering....Pages 383-400 High-Performance Singular Value Decomposition....Pages 401-424 Mining High-Dimensional Scientific Data Sets Using Singular Value Decomposition....Pages 425-438 Spatial Dependence in Data Mining....Pages 439-460 SPARC: Spatial Association Rule-Based Classification....Pages 461-485 What’s Spatial About Spatial Data Mining: Three Case Studies....Pages 487-514 Predicting Failures in Event Sequences....Pages 515-539 Efficient Algorithms for Mining Long Patterns in Scientific Data Sets....Pages 541-566 Probabilistic Estimation in Data Mining....Pages 567-589 Classification Using Association Rules: Weaknesses and Enhancements....Pages 591-605

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.

As data mining has gained acceptance in the commercial world, the important role these techniques play in the scientific and engineering domains is increasingly being overlooked.