Data Mining in Agriculture

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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.

Author(s): Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos (auth.)
Series: Springer Optimization and Its Applications 34
Edition: 1
Publisher: Springer-Verlag New York
Year: 2009

Language: English
Pages: 274
Tags: Operations Research, Mathematical Programming; Math. Appl. in Environmental Science; Mathematical Modeling and Industrial Mathematics; Agriculture

Front Matter....Pages 1-16
Introduction to Data Mining....Pages 1-21
Statistical Based Approaches....Pages 23-45
Clustering by k -means....Pages 47-82
k -Nearest Neighbor Classification....Pages 83-106
Artificial Neural Networks....Pages 107-122
Support Vector Machines....Pages 123-141
Biclustering....Pages 143-160
Validation....Pages 161-172
Data Mining in a Parallel Environment....Pages 173-184
Solutions to Exercises....Pages 185-218
Back Matter....Pages 1-52