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.