提出分布式聚类新初始化方法，提升聚类效果与稳定性。
Distributed Gradient Clustering: Convergence and the Effect of Initialization
- 基于邻居通信的分布式梯度聚类，避免集中式计算
- 新初始化方案使聚类性能优于随机初始化，且更抗初始值影响
- 适合大规模分布式数据场景，对初始化敏感度低
我们研究了中心初始化对一类分布式梯度聚类算法性能的影响，该算法在连通用户网络上运行。每个用户持有本地数据集，仅与相邻节点通信，目标是发现联合数据的全局聚类结果。通过大量数值实验，验证了所提方法相比集中式梯度聚类对初始化更具鲁棒性。受K-means++启发，提出一种新型分布式中心初始化策略，实验表明其性能优于基线随机初始化。
原文摘要 · Abstract (English)
We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the $K$-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization.
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