Título :	Data Mining in Agriculture
Tipo de documento:	documento electrónico
Autores:	Antonio Mucherino ; SpringerLink (Online service) ; Petraq J. Papajorgji ; Panos M. Pardalos
Editorial:	New York, NY : Springer New York
Fecha de publicación:	2009
Colección:	Springer Optimization and Its Applications, ISSN 1931-6828 num. 34
Número de páginas:	XVIII, 274 p. 92 illus
Il.:	online resource
ISBN/ISSN/DL:	978-0-387-88615-2
Idioma :	Inglés (eng)
Palabras clave:	Mathematics  Data  mining  Agriculture  Mathematical  models  Operations  research  Management  science  Environmental  sciences  Modeling  and  Industrial  Mining  Knowledge  Discovery  Research,  Science  Math.  Appl.  in
Clasificación:	51 Matemáticas
Resumen:	Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009
Nota de contenido:	to Data Mining -- Statistical Based Approaches -- Clustering by -means -- -Nearest Neighbor Classification -- Artificial Neural Networks -- Support Vector Machines -- Biclustering -- Validation -- Data Mining in a Parallel Environment -- Solutions to Exercises
En línea:	http://dx.doi.org/10.1007/978-0-387-88615-2
Link:	https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33912

Data Mining in Agriculture [documento electrónico] / Antonio Mucherino ; SpringerLink (Online service) ; Petraq J. Papajorgji ; Panos M. Pardalos . - New York, NY : Springer New York, 2009 . - XVIII, 274 p. 92 illus : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 34) .
ISBN : 978-0-387-88615-2
Idioma : Inglés (eng)

Palabras clave:	Mathematics  Data  mining  Agriculture  Mathematical  models  Operations  research  Management  science  Environmental  sciences  Modeling  and  Industrial  Mining  Knowledge  Discovery  Research,  Science  Math.  Appl.  in
Clasificación:	51 Matemáticas
Resumen:	Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009
Nota de contenido:	to Data Mining -- Statistical Based Approaches -- Clustering by -means -- -Nearest Neighbor Classification -- Artificial Neural Networks -- Support Vector Machines -- Biclustering -- Validation -- Data Mining in a Parallel Environment -- Solutions to Exercises
En línea:	http://dx.doi.org/10.1007/978-0-387-88615-2
Link:	https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33912