Clustering by Hill-Climbing: Consistency Results
arXiv:2202.09023v12 citations
Originality Synthesis-oriented
AI Analysis
This provides theoretical guarantees for clustering algorithms, but it is incremental as it builds on existing hill-climbing formulations from the 1970s.
The paper tackles the problem of clustering using hill-climbing methods, establishing consistency results for both continuous-space and discrete-space variants.
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.