fuzzy k-means

Fuzzy k-means is a clustering method that groups data points into clusters, but unlike traditional methods where each point belongs to only one group, fuzzy k-means allows points to belong to multiple clusters with varying degrees of membership. Think of it as soft assignment: a data point can be, for example, 70% in cluster A and 30% in cluster B. This approach captures overlaps and uncertainties in data, leading to more nuanced and flexible groupings, especially useful when data points are not clearly separated or belong to multiple categories.