arXiv Machine Learning By Savik Kinger, Luciano Dyballa, Steven W. Zucker

Bracketing Uncertainty in Clustering Under the Manifold Hypothesis

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The paper formalizes a geometric tradeoff between ambient separation and sampling gaps to determine when distinct manifold components can be reliably separated in clustering. It introduces a threshold phenomenon for mutual‑k‑nearest‑neighbor graphs, defining an uncertainty zone where the number of clusters cannot be identified. The authors propose Manifold‑Based Clustering (MBC), which outputs a bracket interval quantifying this uncertainty rather than forcing a single cluster count.

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