Fuzzy C Mean

Fuzzy C-means (FCM) is an unsupervised clustering algorithm aiming to partition data into *c* clusters based on fuzzy memberships, addressing the limitations of hard clustering methods. Current research focuses on improving FCM's robustness to noise, imbalanced data, and inefficient convergence through techniques like kernel FCM, novel initialization methods, and adaptive algorithms incorporating graph embeddings or hybrid fuzzy-crisp approaches. These advancements enhance FCM's applicability across diverse fields, including speech enhancement, color classification, and even cryptocurrency price prediction, by improving accuracy and efficiency.