Distributed Gradient Clustering: Convergence and the Effect of Initialization
arXiv:2603.20507v1 Announce Type: new Abstract: 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.
arXiv:2603.20507v1 Announce Type: new Abstract: 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.
Executive Summary
This article presents a comprehensive study on the effects of center initialization on the performance of distributed gradient-based clustering algorithms. The authors propose a novel distributed center initialization scheme inspired by the $K$-means++ initialization, which is shown to improve the performance of their methods compared to baseline random initialization. The study evaluates the resilience of their methods to the effects of initialization and demonstrates superior performance compared to centralized gradient clustering. The findings have significant implications for the development of efficient and scalable clustering algorithms for large datasets.
Key Points
- ▸ The authors propose a novel distributed center initialization scheme inspired by $K$-means++ initialization.
- ▸ The scheme is shown to improve the performance of their methods compared to baseline random initialization.
- ▸ The study evaluates the resilience of their methods to the effects of initialization and demonstrates superior performance compared to centralized gradient clustering.
Merits
Strength in Methodology
The study employs a thorough and systematic approach to evaluating the effects of center initialization on the performance of distributed gradient-based clustering algorithms, providing a comprehensive analysis of the resilience of their methods.
Novel Initialization Scheme
The proposed distributed center initialization scheme inspired by $K$-means++ initialization is a significant contribution to the field, offering improved performance and resilience compared to baseline random initialization.
Demerits
Limitation in Scope
The study focuses on a specific family of distributed gradient-based clustering algorithms, limiting the generalizability of its findings to other types of clustering algorithms.
Assumptions on Network Topology
The study assumes a connected network of users, which may not be representative of all real-world network topologies, potentially limiting the applicability of the results.
Expert Commentary
The study provides a comprehensive analysis of the effects of center initialization on the performance of distributed gradient-based clustering algorithms, making a significant contribution to the field. However, the study's limitations in scope and assumptions on network topology should be carefully considered. The proposed distributed center initialization scheme inspired by $K$-means++ initialization is a novel and promising approach, offering improved performance and resilience compared to baseline random initialization. Future research should explore the generalizability of the study's findings to other types of clustering algorithms and network topologies.
Recommendations
- ✓ Future research should investigate the generalizability of the study's findings to other types of clustering algorithms and network topologies.
- ✓ The proposed distributed center initialization scheme inspired by $K$-means++ initialization should be further explored and validated in real-world applications to confirm its effectiveness and efficiency.
Sources
Original: arXiv - cs.LG
