Document Type

Journal Article

Publication Title

Electronics

Volume

9

Issue

8

First Page

1

Last Page

12

Publisher

MDPI

School

School of Science

Comments

Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics, 9(8), 1295. https://doi.org/10.3390/electronics9081295

Abstract

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

DOI

10.3390/electronics9081295

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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