Fuzzy c-means clustering is an unsupervised learning technique where each data point can belong to multiple clusters with varying degrees of membership. It works by assigning membership values between 0 and 1 to indicate how close each point is to the cluster centers. The algorithm aims to minimize an objective function to determine these optimal membership values and cluster centers. It is useful for overlapping data and outperforms hard clustering methods like k-means.