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English(EN) Sector-Mean: Deterministic Initialization of K-Means Centroids via Angular Sector Partitioning

Sector-Mean 初始化提供更快、更确定的 k-means 聚类

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的确定性方法——Sector-Mean 初始化，用于 k-means 聚类的质心初始化。该方法将数据划分为围绕全局质心的角度扇区，并计算扇区均值，实现了 O(N) 的时间复杂度。在基准数据集和真实世界数据集上的评估表明，与 K-Means++ 相比，Sector-Mean 初始化将初始化时间最多缩短了 74.9%，同时保持了同等的聚类质量并减少了平均迭代次数。 AI

影响 提高了 k-means 聚类的计算效率，可能加速数据分析任务。

排序理由 该集群包含一篇详细介绍 k-means 聚类新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Sector-Mean 初始化提供更快、更确定的 k-means 聚类

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该集群包含一篇详细介绍 k-means 聚类新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Abhiyan Dhakal (Kathmandu University), Pranish Kafle (Kathmandu University), Rajani Chulyadyo (Kathmandu University) ·

    Sector-Mean：通过角度扇区划分确定性地初始化 K-Means 质心

    arXiv:2609.06468v1 Announce Type: new Abstract: K-Means is one of the most widely used clustering algorithms, but its susceptibility to initial centroid selection remains a primary bottleneck for its convergence speed and clustering accuracy. This paper proposes Sector-Mean Initi…

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