Analysis of time complexity of K-means and fuzzy C-means clustering algorithm

Pratik Singh Thakur, Rohit Kumar Verma, Rakesh Tiwari

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.

Full Text: PDF

Published: 2024-04-08

How to Cite this Article:

Pratik Singh Thakur, Rohit Kumar Verma, Rakesh Tiwari, Analysis of time complexity of K-means and fuzzy C-means clustering algorithm, Eng. Math. Lett., 2024 (2024), Article ID 4

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.

Engineering Mathematics Letters

ISSN 2049-9337

Editorial Office: [email protected]

Copyright ©2024 SCIK Publishing Corporation