Fuzzy C-Means is a clustering algorithm that allows data points to belong to multiple clusters with varying degrees of membership, making it suitable for handling overlapping data sets. It is an extension of the k-means algorithm and uses a fuzzy partition matrix to determine the degree of belongingness of each data point to each cluster, optimizing the clustering through iterative updates.