IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-0-387-88615-2_4.html
   My bibliography  Save this book chapter

k-Nearest Neighbor Classification

In: Data Mining in Agriculture

Author

Listed:
  • Antonio Mucherino

    (University of Florida)

  • Petraq J. Papajorgji

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

Abstract

The k-nearest neighbor (k-NN) method is one of the data mining techniques considered to be among the top 10 techniques for data mining [237]. The k-NN method uses the well-known principle of Cicero pares cum paribus facillime congregantur (birds of a feather flock together or literally equals with equals easily associate). It tries to classify an unknown sample based on the known classification of its neighbors. Let us suppose that a set of samples with known classification is available, the so-called training set. Intuitively, each sample should be classified similarly to its surrounding samples. Therefore, if the classification of a sample is unknown, then it could be predicted by considering the classification of its nearest neighbor samples. Given an unknown sample and a training set, all the distances between the unknown sample and all the samples in the training set can be computed. The distance with the smallest value corresponds to the sample in the training set closest to the unknown sample. Therefore, the unknown sample may be classified based on the classification of this nearest neighbor.

Suggested Citation

  • Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "k-Nearest Neighbor Classification," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 83-106, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88615-2_4
    DOI: 10.1007/978-0-387-88615-2_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rafael Rodríguez & Marcos Pastorini & Lorena Etcheverry & Christian Chreties & Mónica Fossati & Alberto Castro & Angela Gorgoglione, 2021. "Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach," Sustainability, MDPI, vol. 13(11), pages 1-17, June.
    2. Peláez-Rodríguez, C. & Pérez-Aracil, J. & Fister, D. & Prieto-Godino, L. & Deo, R.C. & Salcedo-Sanz, S., 2022. "A hierarchical classification/regression algorithm for improving extreme wind speed events prediction," Renewable Energy, Elsevier, vol. 201(P2), pages 157-178.
    3. Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
    4. Arif Jamal Siddiqui & Sadaf Jahan & Maqsood Ahmed Siddiqui & Andleeb Khan & Mohammed Merae Alshahrani & Riadh Badraoui & Mohd Adnan, 2023. "Targeting Monoamine Oxidase B for the Treatment of Alzheimer’s and Parkinson’s Diseases Using Novel Inhibitors Identified Using an Integrated Approach of Machine Learning and Computer-Aided Drug Desig," Mathematics, MDPI, vol. 11(6), pages 1-17, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-0-387-88615-2_4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.