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© 2009

Data Mining in Agriculture

Benefits

  • 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

Textbook

Part of the Springer Optimization and Its Applications book series (SOIA, volume 34)

Table of contents

  1. Front Matter
    Pages 1-16
  2. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 1-21
  3. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 23-45
  4. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 47-82
  5. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 83-106
  6. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 107-122
  7. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 123-141
  8. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 143-160
  9. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 161-172
  10. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 173-184
  11. Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos
    Pages 185-218
  12. Back Matter
    Pages 1-52

About this book

Introduction

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.

Keywords

Agricultural Planning Agriculture Systems Clustering SOIA algorithms artificial networks classification data mining data mining techniques k-means methods vector machines

Authors and affiliations

  1. 1.Information Technology Office, Institute of Food & AgriculturalUniversity of FloridaGainesvilleUSA
  2. 2., Department of Industrial and Systems EngUniversity of FloridaGainesvilleUSA
  3. 3.Dept. Industrial & Systems, EngineeringUniversity of FloridaGainesvilleUSA

Bibliographic information

Industry Sectors
Finance, Business & Banking

Reviews

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)