Stable and consistent density-based clustering
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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.
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