Data Mining in Agriculture

Overview

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®.

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Paperback (2009)
$48.07
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Overview

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®.

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Editorial Reviews

From the Publisher
From the reviews:
“This book covers several topics in data mining within the context of agriculture. … Every problem at the end of each chapter is provided with solutions … . who are looking for a first step into the field of data mining in agriculture may appreciate this broad nature … . Students interested in a hands-on approach using MATLAB may also find the book useful due to the sample solutions provided.” (R. Wan, Journal of the Operational Research Society, Vol. 61, 2010)
“The book … presents in a comprehensive way most up-to-date data mining techniques and their application to problems from agriculture domain. … Researchers, practitioners and students will find the book very useful … . Researchers will find in the book not only a good reference and a compendium of most important techniques but also an ‘all in one place’ analysis of most important data mining techniques … . Teachers can use the book for data mining subjects in undergraduate and graduate studies … .”­­­ (Fatos Xhafa, Journal of Global Optimization, Vol. 48, 2010)
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Product Details

Table of Contents

Introduction to Data Mining.- 2 Statistical Methods.- 3 Clustering by k-means.- 4 k-nearest Neighbor Classification.- 5 Artificial Neural Networks.- 6 Support Vector Machines.- 7 Biclustering.- 8 Validation.- 9 An Application in C.- 10 Data Mining in a Parallel Environment.- 11 Solutions of the Exercises.- A. Matlab Environment.- B. C programming language.- C. Message Passing Interface (MPI).- .D. Eigenvalues and Eigenvectors.- References.

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