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Data Mining in Agriculture [electronic resource] /by Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos.

by Mucherino, Antonio [author.]; Papajorgji, Petraq J [author.]; Pardalos, Panos M [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Optimization and Its Applications: 34Publisher: New York, NY : Springer New York, 2009.Description: XVIII, 274p. 92 illus. online resource.ISBN: 9780387886152.Subject(s): Mathematics | Agriculture | Operations research | Environmental sciences | Mathematics | Operations Research, Mathematical Programming | Math. Appl. in Environmental Science | Mathematical Modeling and Industrial Mathematics | AgricultureDDC classification: 519.6 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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Item type Current location Call number Status Date due Barcode
T57.6-57.97 (Browse shelf) Available
Long Loan MAIN LIBRARY
QA402-402.37 (Browse shelf) Available

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.

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.

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