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Data Mining for Scientific and Engineering Applications

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Cover of 'Data Mining for Scientific and Engineering Applications'

Table of Contents

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    Book Overview
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    Chapter 1 On Mining Scientific Datasets
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    Chapter 2 Understanding High Dimensional and Large Data Sets: Some Mathematical Challenges and Opportunities
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    Chapter 3 Data Mining at the Interface of Computer Science and Statistics
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    Chapter 4 Mining Large Image Collections
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    Chapter 5 Mining Astronomical Databases
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    Chapter 6 Searching for Bent-Double Galaxies in the First Survey
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    Chapter 7 A Dataspace Infrastructure for Astronomical Data
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    Chapter 8 Data Mining Applications in Bioinformatics
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    Chapter 9 Mining Residue Contacts in Proteins
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    Chapter 10 KDD Services at the Goddard Earth Sciences Distributed Active Archive Center
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    Chapter 11 Data Mining in Integrated Data Access and Data Analysis Systems
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    Chapter 12 Spatial Data Mining for Classification, Visualisation and Interpretation with Artmap Neural Network
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    Chapter 13 Real Time Feature Extraction for the Analysis of Turbulent Flows
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    Chapter 14 Data Mining for Turbulent Flows
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    Chapter 15 EVITA — Efficient Visualization and Interrogation of Tera-Scale Data
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    Chapter 16 Towards Ubiquitous Mining of Distributed Data
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    Chapter 17 Decomposable Algorithms for Data Mining
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    Chapter 18 HDDI™: Hierarchical Distributed Dynamic Indexing
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    Chapter 19 Parallel Algorithms for Clustering High-Dimensional Large-Scale Datasets
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    Chapter 20 Efficient Clustering of Very Large Document Collections
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    Chapter 21 A Scalable Hierarchical Algorithm for Unsupervised Clustering
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    Chapter 22 High-Performance Singular Value Decomposition
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    Chapter 23 Mining High-Dimensional Scientific Data Sets Using Singular Value Decomposition
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    Chapter 24 Spatial Dependence in Data Mining
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    Chapter 25 SPARC: Spatial Association Rule-Based Classification
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    Chapter 26 What’s Spatial About Spatial Data Mining: Three Case Studies
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    Chapter 27 Predicting Failures in Event Sequences
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    Chapter 28 Efficient Algorithms for Mining Long Patterns in Scientific Data Sets
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    Chapter 29 Probabilistic Estimation in Data Mining
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    Chapter 30 Classification Using Association Rules: Weaknesses and Enhancements
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