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Scientific data mining : a practical perspective / Chandrika Kamath.
Tipo de material:
TextoDetalles de publicación: Philadelphia : Society for Industrial and Applied Mathematics, 2009.Descripción: xviii, 286 p. : il., gráficas ; 25 cmISBN: - 9780898716757
- 0898716756
- QA 76.9.D343 K356.2009
Contenidos:
Introduction -- Data mining in science and engineering -- Common themes in mining scientific data -- The scientific data mining process -- Reducing the size of the data -- Fusing different data modalities -- Enhancing image data -- Finding objects in the data -- Extracting features describing the objects -- Reducing the dimension of the data -- Finding patterns in the data -- Visualizing the data and validating the results -- Scientific data mining systems -- Lessons learned, challenges, and opportunities.
| Tipo de ítem | Biblioteca actual | Colección | Signatura topográfica | Copia número | Estado | Fecha de vencimiento | Código de barras | |
|---|---|---|---|---|---|---|---|---|
| Libros | Biblioteca Francisco Xavier Clavigero Acervo | Acervo General | QA 76.9.D343 K356.2009 (Navegar estantería(Abre debajo)) | ej. 1 | Disponible | UIA029558 |
Navegando Biblioteca Francisco Xavier Clavigero estanterías, Ubicación en estantería: Acervo, Colección: Acervo General Cerrar el navegador de estanterías (Oculta el navegador de estanterías)
| QA 76.9.D343 H47.2005 Introducción a la minería de datos/ | QA 76.9.D343 J33.2000 The Microsoft SQL server 2000 analysis services step by step / | QA 76.9.D343 J33.2000 The Microsoft SQL server 2000 analysis services step by step / | QA 76.9.D343 K356.2009 Scientific data mining : a practical perspective / | QA 76.9.D343 K36.2003 Data mining : concepts, models, methods, and algorithms / | QA 76.9.D343 M36.2004 Managing data mining : advice from experts / | QA 76.9.D343 M57.2013 Clustering : a data recovery approach / |
Incluye referencias bibliográficas (325-277) e índice.
Introduction -- Data mining in science and engineering -- Common themes in mining scientific data -- The scientific data mining process -- Reducing the size of the data -- Fusing different data modalities -- Enhancing image data -- Finding objects in the data -- Extracting features describing the objects -- Reducing the dimension of the data -- Finding patterns in the data -- Visualizing the data and validating the results -- Scientific data mining systems -- Lessons learned, challenges, and opportunities.
