“K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. … Studies show the equivalence of K-means to principal component analysis, non-negative matrix factorization, and spectral clustering. However, these studies focus on standard K-means with squared euclidean distance. In this review paper, we unify the available approaches in generalizing K-means to solve challenging and complex problems. We show that these generalizations can be seen from four aspects: data representation, distance measure, label assignment and centroid updating.”

Find the paper and full list of authors at IEEE Transactions on Pattern Analysis and Machine Intelligence.