Data Mining for Scientific and Engineering - gebrauchtes Buch

EAN (ISBN-13): 9781402001147
ISBN (ISBN-10): 1402001142
Taschenbuch
Erscheinungsjahr: 2001
Herausgeber: Springer US
628 Seiten
Gewicht: 0,935 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-11-07T18:30:39+01:00 (Berlin)
Detailseite zuletzt geändert am 2023-11-28T23:46:52+01:00 (Berlin)
ISBN/EAN: 9781402001147

ISBN - alternative Schreibweisen:
1-4020-0114-2, 978-1-4020-0114-7
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: grossman, kamath, kegel, kumar
Titel des Buches: data mining for scientific engineering applications, scientific forth, scientific and engineering

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Daten vom Verlag:

Autor/in: R.L. Grossman; C. Kamath; P. Kegelmeyer; V. Kumar; R. Namburu
Titel: Massive Computing; Data Mining for Scientific and Engineering Applications
Verlag: Springer; Springer US
605 Seiten
Erscheinungsjahr: 2001-10-31
New York; NY; US
Sprache: Englisch
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.