Fuzzy Clustering

Fuzzy clustering is a machine learning technique that groups data points into clusters with varying degrees of membership, accommodating inherent uncertainty and vagueness. Current research emphasizes improving existing algorithms like Fuzzy C-Means (FCM) through novel initialization methods, adaptive hyperparameter learning, and the development of new validity indices to assess cluster quality, particularly for challenging data types such as time series (including circular and ordinal data) and high-dimensional data like color spaces and word embeddings. These advancements enhance the applicability of fuzzy clustering across diverse fields, including anomaly detection, color classification, economic forecasting, and forensic document analysis, by providing more robust and efficient clustering solutions.