Analysis of time complexity of K-means and fuzzy C-means clustering algorithm
Abstract
This paper compares the time complexity of the K-means and fuzzy C-means (FCM) clustering algorithms for different cluster counts. The algorithms’ performance is evaluated using several datasets, and the results show that, while the FCM algorithm has a higher time complexity than the K-means algorithm in general, it may be better suited for certain types of data and when a larger number of clusters are used. The paper concludes that both algorithms have advantages and disadvantages, and that the choice should be based on the specific requirements of the problem at hand.
- Published
- 8 April 2024
- Volume
- 2024 (2024)
- License
- CC BY 3.0
Copyright © 2024 Pratik Singh Thakur, Rohit Kumar Verma, Rakesh Tiwari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
P. S. Thakur, R. K. Verma, R. Tiwari, Analysis of time complexity of K-means and fuzzy C-means clustering algorithm, Eng. Math. Lett. 2024 (2024), 4. https://doi.org/10.28919/eml/8402
BibTeX
@article{Thakur2024,
author = {Thakur, Pratik Singh and Verma, Rohit Kumar and Tiwari, Rakesh},
title = {Analysis of time complexity of K-means and fuzzy C-means clustering algorithm},
journal = {Eng. Math. Lett.},
volume = {2024},
year = {2024},
pages = {4},
doi = {10.28919/eml/8402}
}