Data Mining in Agriculture

Antonio Mucherino, Petraq Papajorgji, Panos M. Pardalos

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English

Springer-Verlag New York Inc.
19 August 2009

Linear programming; Mathematical modelling; Maths for engineers; Environmental science, engineering & technology; Agriculture & farming; Agricultural science; Data mining; Expert & knowledge-based systems

Series: Springer Optimization and Its Applications

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.

By: Antonio Mucherino, Petraq Papajorgji, Panos M. Pardalos
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States
Edition: 2009 ed.
Volume: 34
Dimensions: Height: 235mm, Width: 155mm, Spine: 17mm
Weight: 1.290kg
ISBN: 9780387886145
ISBN 10: 0387886141
Series: Springer Optimization and Its Applications
Pages: 274
Publication Date: 19 August 2009
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

Reviews for Data Mining in Agriculture

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)