Data Mining for Scientific and Engineering Applications

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.