Work

Distributed Gradient Clustering: Convergence and the Effect of Initialization

Year: 2024

Type: conference-paper

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.

Source: 2018 52nd Asilomar Conference on Signals, Systems, and Computers

DOI: 10.1109/ieeeconf60004.2024.10942834

Authors: Aleksandar Armacki, Himkant Sharma, Dragana Bajović, Dušan Jakovetić, Mrityunjoy Chakraborty, Soummya Kar

Institutions: Carnegie Mellon University, Indian Institute of Technology Kharagpur, University of Novi Sad

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: Collaborative Research: Scalable Circuit theoretic Framework for Large Grid Simulations and Optimizations: from Combined T&D Planning to Electromagnetic Transients