提出新聚类一致性度量，评估聚类大小分布均匀性。
The Mass Agreement Score: A Point-centric Measure of Cluster Size Consistency
- 从点的角度定义聚类大小一致性度量，避免标签数量变化干扰。
- 分数在[0,1]区间内，相似结构的聚类得分相近。
- 适合评估聚类结果稳定性，尤其对标签数不固定场景有用。
在聚类分析中，某一簇显著主导其他簇通常不理想，因此需要一种衡量簇大小均匀性的指标来筛选此类划分。该指标的基本要求是稳定性：仅在点分配上略有差异的划分应获得相近的均匀性评分。难点在于聚类标签并非固定对象，算法在底层点分布微小变化时可能产生不同数量的标签，直接基于标签定义的度量因此容易在标签数扰动下失稳。本文提出点中心的质心一致性分数（Mass Agreement Score, MAS），一个取值范围为[0,1]的度量，从每个簇中点的视角评估其期望簇大小的一致性。该构造天然具备片段鲁棒性，对具有相似整体结构的划分赋予相近评分，同时对簇质量的真实再分配仍保持敏感。
原文摘要 · Abstract (English)
In clustering, strong dominance in the size of a particular cluster is often undesirable, motivating a measure of cluster size uniformity that can be used to filter such partitions. A basic requirement of such a measure is stability: partitions that differ only slightly in their point assignments should receive similar uniformity scores. A difficulty arises because cluster labels are not fixed objects; algorithms may produce different numbers of labels even when the underlying point distribution changes very little. Measures defined directly over labels can therefore become unstable under label-count perturbations. I introduce the Mass Agreement Score (MAS), a point-centric metric bounded in [0, 1] that evaluates the consistency of expected cluster size as measured from the perspective of points in each cluster. Its construction yields fragment robustness by design, assigning similar scores to partitions with similar bulk structure while remaining sensitive to genuine redistribution of cluster mass.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。