Possibilistic c-means

Possibilistic C-Means (PCM) is a clustering algorithm used to group data points based on similarity, allowing for uncertainty and overlapping clusters. Unlike traditional methods, PCM assigns a degree of belonging to each cluster for every point, reflecting how well the point fits. It focuses on the quality of fit rather than strict membership rules, making it better at handling noise or outliers. This flexibility enables PCM to identify meaningful groups even in complex, noisy data, making it useful in pattern recognition and data analysis where clarity of group boundaries may be ambiguous.