Comprehensive Review of K-Means Clustering Algorithms

Authors

  • Eric U. Oti Department of Statistics, Federal Polytechnic Ekowe, Bayelsa State, Nigeria
  • Michael O. Olusola Nnamdi Azikiwe University, Awka Anambra State, Nigeria
  • Francis C. Eze Nnamdi Azikiwe University, Awka Anambra State, Nigeria
  • Samuel U. Enogwe Michael Okpara University of Agriculture, Umudike, Nigeria

DOI:

https://doi.org/10.31695/IJASRE.2021.34050

Keywords:

Centroid, Cluster Analysis, Euclidean Distance, K-Means

Abstract

This paper presents a comprehensive review of existing techniques of k-means clustering algorithms made at various times. The k-means algorithm is aimed at partitioning objects or points to be analyzed into well separated clusters. There are different algorithms for k-means clustering of objects such as traditional k-means algorithm, standard k-means algorithm, basic k-means algorithm and the conventional k-means algorithm, this are perhaps the most widely used versions of the k-means algorithms. These algorithms uses the Euclidean distance as its metric and minimum distance rule approach by assigning each data points (objects) to its closest centroids.

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How to Cite

Eric U. Oti, Michael O. Olusola, Francis C. Eze, & Samuel U. Enogwe. (2021). Comprehensive Review of K-Means Clustering Algorithms. International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE, 7(8), 64–69. https://doi.org/10.31695/IJASRE.2021.34050

Issue

Vol. 7 No. 8 (2021): August-2021

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Articles

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Copyright (c) 2021 Eric U. Oti, Michael O. Olusola, Francis C. Eze, Samuel U. Enogwe

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