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Data Mining for Scientific and Engineering - Grossman
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ISBN: 9781402001147

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Data Mining for Scientific and Engineering Applications  Softcover reprint of the original 1st ed. 2001 - Grossman, R.L., C. Kamath  und P. Kegelmeyer
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Grossman, R.L., C. Kamath und P. Kegelmeyer:

Data Mining for Scientific and Engineering Applications Softcover reprint of the original 1st ed. 2001 - Pasta blanda

2001, ISBN: 9781402001147

Softcover reprint of the original 1st ed. 2001 Gepflegter, sauberer Zustand. Außen: angestoßen. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. 1432320/202 Versa… Más…

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Data Mining for Scientific and Engineering Applications - Grossman, R.L., C. Kamath  und P. Kegelmeyer
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Grossman, R.L., C. Kamath und P. Kegelmeyer:
Data Mining for Scientific and Engineering Applications - Pasta blanda

2001

ISBN: 9781402001147

[PU: Springer US], Gepflegter, sauberer Zustand. Außen: angestoßen. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. 1432320/202, DE, [SC: 0.00], gebraucht; sehr… Más…

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Data Mining for Scientific and Engineering Applications - Grossman, R.L., C. Kamath  und P. Kegelmeyer
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Grossman, R.L., C. Kamath und P. Kegelmeyer:
Data Mining for Scientific and Engineering Applications - Pasta blanda

2001, ISBN: 9781402001147

[PU: Springer US], Gepflegter, sauberer Zustand. Außen: angestoßen. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. 1432320/202, DE, [SC: 0.00], gebraucht; sehr… Más…

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Data Mining for Scientific and Engineering Applications - Pasta blanda

2001, ISBN: 9781402001147

*Data Mining for Scientific and Engineering Applications* - Softcover reprint of the original 1st ed. 2001 / Taschenbuch für 213.99 € / Aus dem Bereich: Bücher, Ratgeber, Computer & Inter… Más…

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Detalles del libro
Data Mining for Scientific and Engineering Applications

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Detalles del libro - Data Mining for Scientific and Engineering Applications


EAN (ISBN-13): 9781402001147
ISBN (ISBN-10): 1402001142
Tapa blanda
Año de publicación: 2001
Editorial: Springer US
628 Páginas
Peso: 0,935 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2007-11-07T11:30:39-06:00 (Mexico City)
Página de detalles modificada por última vez el 2023-11-28T16:46:52-06:00 (Mexico City)
ISBN/EAN: 1402001142

ISBN - escritura alterna:
1-4020-0114-2, 978-1-4020-0114-7
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: grossman, kamath, kegel, kumar
Título del libro: data mining for scientific engineering applications, scientific forth, scientific and engineering


Datos del la editorial

Autor: R.L. Grossman; C. Kamath; P. Kegelmeyer; V. Kumar; R. Namburu
Título: Massive Computing; Data Mining for Scientific and Engineering Applications
Editorial: Springer; Springer US
605 Páginas
Año de publicación: 2001-10-31
New York; NY; US
Idioma: Inglés
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.

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