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Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey

In: Advances in Modeling Agricultural Systems

Author

Listed:
  • Radnaabazar Chinchuluun
  • Won Suk Lee
  • Jevin Bhorania
  • Panos M. Pardalos

    (University of Florida)

Abstract

Data mining has become an important tool for information analysis in many disciplines. Data clustering, also known as unsupervised classification, is a popular data-mining technique. Clustering is a very challenging task because of little or no prior knowledge. Literature review reveals researchers’ interest in development of efficient clustering algorithms and their application to a variety of real-life situations. This chapter presents fundamental concepts of widely used classification algorithms including k-means, k-nearest neighbor, artificial neural networks, and fuzzy c-means. We also discuss applications of these algorithms in food and agriculture sciences including fruits classification, machine vision, wine classification, and analysis of remotely sensed forest images.

Suggested Citation

  • Radnaabazar Chinchuluun & Won Suk Lee & Jevin Bhorania & Panos M. Pardalos, 2009. "Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey," Springer Optimization and Its Applications, in: Panos M. Pardalos & Petraq J. Papajorgji (ed.), Advances in Modeling Agricultural Systems, pages 433-454, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-75181-8_21
    DOI: 10.1007/978-0-387-75181-8_21
    as

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