提出标签一致性新标准，让聚类结果更稳定可靠。
Clustering with Label Consistency
- 以点的标签连续性定义聚类一致性，关注实际分配稳定性
- 针对k中心和k中位问题设计新算法，提升聚类结果一致性
- 适合需要稳定聚类结果的应用场景，如用户分群、图像标注
设计高效、有效且一致的度量聚类算法是一项日益受到关注的重要挑战。传统方法侧重于簇中心的稳定性，却忽视了现实需求中的标签稳定性，即点对命名簇的稳定分配。本文首次系统研究标签一致性的度量聚类问题，引入新的一致性概念，通过计算两个连续解之间的标签距离进行衡量。基于此定义，我们为经典的k-中心和k-中位问题设计了新的具有一致性保证的近似算法。
原文摘要 · Abstract (English)
Designing efficient, effective, and consistent metric clustering algorithms is a significant challenge attracting growing attention. Traditional approaches focus on the stability of cluster centers; unfortunately, this neglects the real-world need for stable point labels, i.e., stable assignments of points to named sets (clusters). In this paper, we address this gap by initiating the study of label-consistent metric clustering. We first introduce a new notion of consistency, measuring the label distance between two consecutive solutions. Then, armed with this new definition, we design new consistent approximation algorithms for the classical $k$-center and $k$-median problems.
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