K-Survival Means
2026-07-27 • Machine Learning
Machine Learning
AI summaryⓘ
The authors introduce K-SurvMeans, a new way to group data based on survival times by improving the traditional K-Means method. Their approach focuses on making groups that differ clearly in survival outcomes by directly using survival information to find the best group centers. Because the math involved is tricky and not smooth, they use a particle swarm optimization technique to solve it. They also reduce the data to fewer dimensions before clustering to make the process more efficient and effective. Tests on public datasets show their method makes better-separated survival groups compared to other modern methods.
K-Means clusteringsurvival analysisparticle swarm optimizationnon-differentiable optimizationdimensionality reductionlatent spacesurvival datacluster centersbenchmark datasets
Authors
Abdallah Alabdallah
Abstract
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.