Clustering by Hill-Climbing: Consistency Results

Arias-Castro, Ery, Qiao, Wanli

arXiv.org Machine Learning 

We consider several hill-climbing approaches to clustering as formulated by Fukunaga and Hostetler in the 1970's. We study both continuous-space and discrete-space (i.e., medoid) variants and establish their consistency.

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