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arXiv · 1708.05254

Adaptive Clustering Using Kernel Density Estimators

Ingo Steinwart·Bharath K. Sriperumbudur·Philipp Thomann
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Abstract

We derive and analyze a generic, recursive algorithm for estimating all splits in a finite cluster tree as well as the corresponding clusters. We further investigate statistical properties of this generic clustering algorithm when it receives level set estimates from a kernel density estimator. In particular, we derive finite sample guarantees, consistency, rates of convergence, and an adaptive data-driven strategy for choosing the kernel bandwidth. For these results we do not need continuity assumptions on the density such as H\"{o}lder continuity, but only require intuitive geometric assumptions of non-parametric nature.

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Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann. 2017-08-17. Adaptive Clustering Using Kernel Density Estimators. https://arxiv.org/abs/1708.05254

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