CavMerge: Merging K-means Based on Local Log-Concavity
K-means clustering, a classic and widely-used clustering technique, is known to exhibit suboptimal performance when applied to non-linearly separable data. Numerous adjustments and modifications have been proposed to address this issue, including methods that merge K-means results from a relatively large K to obtain a final cluster assignment. However, existing methods of this nature often encounter computational inefficiencies and suffer from hyperparameter tuning. Here we present \emph{CavMerge}, a novel K-means merging algorithm that is intuitive, free of parameter tuning, and computationally efficient. Operating under minimal local distributional assumptions, our algorithm demonstrate...
| Authors | Zhili Qiao, Wangqian Ju, Peng Liu |
|---|---|
| Primary category | stat.ME |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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