In this chapter, we put the distance metrics and dissimilarity measures from the last chapter into a good use: data clustering. We first introduce the basic heuristics in relation to the main approaches for clustering. Key algorithms in different approaches are then introduced: k-means and its variants, density-based methods, and hierarchical clustering etc.