Empirical evidence shows the overwhelming of FCM compared to K-Means, in terms of performance results (i.e., cluster accuracy), even though it needs more computation time than K-Means clustering.
In literature, partitive clustering algorithms such as K-means, FCM are used as a baseline and compared for data classification. It is widely employed in several domains, such as information retrieval, image segmentation, medical imaging, etc. The FCM algorithm is one of the most known partitive clustering algorithms. Sabrina Senatore, in Statistical Modeling in Machine Learning, 2023 11.4.2 FCM and weighted FCM algorithms Emotion-based classification through fuzzy entropy-enhanced FCM clusteringīarbara Cardone.
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