Hierarchical and Density-Based Clustering
k-means needs to be told $k$ in advance, and assumes every cluster is roughly round. This page covers two families that relax those assumptions in different directions: hierarchical clustering defers the choice of cluster count to a visual cut, and density-based clustering abandons the "round cluster" assumption entirely by defining a cluster as a connected region of high density.