# Distributed k-means algorithm

arXiv: https://arxiv.org/abs/1312.4176 · HTML: https://mlsanity.com/paper/1312.4176/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2013.

## Built on by (most cited first, 3 of 3)

- [A Survey From Distributed Machine Learning to Distributed Deep Learning](https://mlsanity.com/paper/2307.05232/) (2023)
- [A Unified Framework for Center-based Clustering of Distributed Data](https://mlsanity.com/paper/2402.01302/) (2024)
- [Distributed Gradient Clustering: Convergence and the Effect of Initialization](https://mlsanity.com/paper/2603.20507/) (2026)

## Abstract (arXiv)

In this paper we provide a fully distributed implementation of the k-means clustering algorithm, intended for wireless sensor networks where each agent is endowed with a possibly high-dimensional observation (e.g., position, humidity, temperature, etc.) The proposed algorithm, by means of one-hop communication, partitions the agents into measure-dependent groups that have small in-group and large out-group "distances". Since the partitions may not have a relation with the topology of the network--members of the same clusters may not be spatially close--the algorithm is provided with a mechanism to compute the clusters'centroids even when the clusters are disconnected in several sub-clusters.The results of the proposed distributed algorithm coincide, in terms of minimization of the objective function, with the centralized k-means algorithm. Some numerical examples illustrate the capabilities of the proposed solution.