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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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Data Mining for Scientific and Engineering Applications
Published by
Springer US, January 2001
DOI 10.1007/978-1-4615-1733-7
978-1-4020-0114-7, 978-1-4615-1733-7

Grossman, Robert L., Kamath, Chandrika, Kegelmeyer, Philip, Kumar, Vipin, Namburu, Raju R.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 10%
Brazil 1 2%
South Africa 1 2%
China 1 2%
Japan 1 2%
India 1 2%
Unknown 39 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 29%
Student > Ph. D. Student 10 20%
Professor > Associate Professor 7 14%
Unspecified 4 8%
Student > Master 4 8%
Other 6 12%
Unknown 4 8%
Readers by discipline Count As %
Computer Science 15 31%
Engineering 6 12%
Agricultural and Biological Sciences 6 12%
Unspecified 5 10%
Chemistry 4 8%
Other 9 18%
Unknown 4 8%