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. Language: English