Distributed k-means algorithm
Abstract
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.
Cite
@article{arxiv.1312.4176,
title = {Distributed k-means algorithm},
author = {Gabriele Oliva and Roberto Setola and Christoforos N. Hadjicostis},
journal= {arXiv preprint arXiv:1312.4176},
year = {2014}
}
Comments
preprint submitted to IEEE transactions on mobile computing