Stable and consistent density-based clustering

We present a consistent approach to density-based clustering, which satisfies a stability theorem that holds without any distributional assumptions. We also show that the algorithm can be combined with standard procedures to extract a flat clustering from a hierarchical clustering, and that the resulting flat clustering algorithms satisfy stability theorems. The algorithms and proofs are inspired by topological data analysis.