基于删失社交网络数据聚类的用户生存类型识别
Identifying User Survival Types via Clustering of Censored Social Network Data
摘要：
生存数据聚类分析的目标是识别与生存结果明显相关的聚类。先前的研究主要在具有相对小的数据集的医学领域中探索了这个问题，但是在具有大数据集的其他领域（例如社交网络）中可能出现对这样的聚类方法的需要。具体地说，我们希望通过基于用户在网络中的寿命对用户进行聚类来识别社交网络中的不同生存类。在本文中，我们提出了一个基于决策树的算法，使用一个全球规范化的$p$值，以确定集群具有显着不同的生存分布。我们通过一个简单的生存预测任务来评估我们模型中的聚类，并表明我们的模型优于其他竞争方法。
英文摘要：
The goal of cluster analysis in survival data is to identify clusters that are decidedly associated with the survival outcome. Previous research has explored this problem primarily in the medical domain with relatively small datasets, but the need for such a clustering methodology could arise in other domains with large datasets, such as social networks. Concretely, we wish to identify different survival classes in a social network by clustering the users based on their lifespan in the network. In this paper, we propose a decision tree based algorithm that uses a global normalization of $p$-values to identify clusters with significantly different survival distributions. We evaluate the clusters from our model with the help of a simple survival prediction task and show that our model outperforms other competing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
