This book covers data mining techniques applied to agricultural and environmental fields. It offers theoretical and practical insights and focuses on the context of each data mining technique. It includes many examples and exercises with solutions.

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.

First textbook in data mining in agriculture Presentation suitable for students, researchers, and professionals, in the classroom or as a self-study Explores examples in agriculture/environmental fields Provides Matlab codes to illustrate examples Includes numerous exercises and some solutions

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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.

Contenu
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.