Data Mining in Agriculture

ISBN-13: 9780387886145 / Angielski / Twarda / 2009 / 274 str.

Antonio Mucherino; Petraq J. Papajorgji; Panos M. Pardalos

Opis

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(R). 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.

Recenzje

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)

Spis treści

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.

Blurb

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.