English(EN) Randomizing the Number of Centers in k-means++

k-means++ 算法改进以提高近似比

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-30 04:00

研究人员提出了一种对 k-means++ 算法的修改，k-means++ 是一种用于初始化 k-means 聚类的常用方法。标准算法对于固定数量的中心 k，其最坏情况下的期望近似比为 \Theta(\log k)。然而，当中心数量 k 从范围 \{K, \ldots, 2K-1\} 中均匀选择时，修改后的 k-means++ 算法能以恒定概率实现 O(1) 近似。 AI

影响这项研究可能带来更高效、更准确的机器学习聚类应用。

排序理由该集群包含一篇详细介绍新算法方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Vaclav Rozhon · 2026-07-30 04:00

k-means++ 中心数量的随机化

arXiv:2607.26202v1 Announce Type: cross Abstract: The $k$-means++ algorithm is a standard and widely used seeding method for $k$-means clustering, but for a fixed number $k$ of centers its worst-case expected approximation ratio is $\Theta(\log k)$. We consider the same algorithm…

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k-means++ 中心数量的随机化

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