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 to indicate the likelihood of a data point’s association with each cluster. This soft clustering approach is particularly useful for datasets with overlapping clusters, as it provides a more nuanced understanding of data distribution. FCM iteratively updates cluster centers and membership values to minimize the weighted distances between data points and cluster centers, enhancing clustering accuracy.