Data Mining for Scientific and Engineering - livro usado

EAN (ISBN-13): 9781402001147
ISBN (ISBN-10): 1402001142
Livro de bolso
Ano de publicação: 2001
Editor/Editora: Springer US
628 Páginas
Peso: 0,935 kg
Língua: eng/Englisch

Livro na base de dados desde 2007-11-07T15:30:39-02:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-11-28T19:46:52-03:00 (Sao Paulo)
Número ISBN/EAN: 1402001142

Número ISBN - Ortografia alternativa:
1-4020-0114-2, 978-1-4020-0114-7
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: grossman, kamath, kegel, kumar
Título do livro: data mining for scientific engineering applications, scientific forth, scientific and engineering

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Dados da editora

Autor: R.L. Grossman; C. Kamath; P. Kegelmeyer; V. Kumar; R. Namburu
Título: Massive Computing; Data Mining for Scientific and Engineering Applications
Editora: Springer; Springer US
605 Páginas
Ano de publicação: 2001-10-31
New York; NY; US
Língua: Inglês
213,99 € (DE)
219,99 € (AT)
236,00 CHF (CH)
Available
XX, 605 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Algorithmen und Datenstrukturen; Verstehen; algorithms; bioinformatics; classification; clustering; computer science; data analysis; data mining; database; databases; information; network; neural networks; research; Service; statistics; Data Structures and Information Theory; Artificial Intelligence; Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Technology and Engineering; Theory of Computation; Informationstheorie; Künstliche Intelligenz; Wahrscheinlichkeitsrechnung und Statistik; Ingenieurswesen, Maschinenbau allgemein; Theoretische Informatik; BB; EA

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.