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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
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    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. 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.
    3. 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.
    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.

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