发表机构
Center for Applied Intelligent Systems Research (CAISR)（应用智能系统研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对生存数据聚类提出K-SurvMeans方法，在聚类中用生存结果优化中心，因优化问题不可微采用粒子群算法，还扩展到低维潜在空间，实验证明该方法比现有深度学习生存聚类方法能产生分离性更好的聚类。
AI 中文摘要
在这项工作中，我们提出了K-SurvMeans，这是一种用于聚类生存数据的K均值新扩展。该方法在聚类过程中明确使用生存结果来优化聚类中心，从而最大化聚类之间的成对生存差异。目标函数促使聚类从生存角度更好地分离。由于产生的优化问题不可微，我们采用粒子群算法进行优化。为进一步提高灵活性并减轻维度诅咒，我们将框架扩展到在通过降维获得的低维潜在空间中运行。实验表明，与现有的基于深度学习的生存聚类方法相比，K-SurvMeans能持续产生生存分布分离性更好的聚类。
英文摘要
In this work, we propose K-SurvMeans, a novel extension of K-Means for clustering survival data. The method explicitly uses the survival outcome in the clustering process to optimize cluster centers, thereby maximizing pairwise survival differences between clusters. The objective function encourages the clusters to be well-separated from the survival perspective. Since the resulting optimization problem is non-differentiable, we employ the Particle Swarm algorithm for the Optimization process. To further improve flexibility and mitigate the curse of dimensionality, we extend the framework to operate in a learned low-dimensional latent space obtained via a dimensionality reduction. This allows the method to capture better-separated clusters and enhance optimization efficiency by reducing the search space. Experiments on multiple publicly available benchmark survival datasets demonstrate that K-SurvMeans consistently yields clusters with improved separation in survival distributions compared to existing deep learning-based survival clustering methods.
