Data Mining in Agriculture

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

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

  • Antonio Mucherino
    • 1
  • Petraq J. Papajorgji
    • 2
  • Panos M. Pardalos
    • 3
  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

  • DOI https://doi.org/10.1007/978-0-387-88615-2
  • Copyright Information Springer Science+Business Media, LLC 2009
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-88614-5
  • Online ISBN 978-0-387-88615-2
  • Series Print ISSN 1931-6828
  • About this book