Fuzzy C Means

Fuzzy C-Means (FCM) is an unsupervised clustering algorithm that allows data points to belong to multiple clusters with varying degrees of membership. Unlike traditional hard clustering methods, where each data point is assigned to a single cluster, FCM assigns a probability value between 0 and 1 for each point’s association with each cluster. This approach is particularly useful for datasets with overlapping clusters, as it provides a more nuanced understanding of data relationships. The algorithm iteratively updates cluster centers and membership values until convergence, making it a powerful tool for data analysis in various fields, including pattern recognition and image processing.