Optimal initialization of K-means using Particle Swarm Optimization
Machine Learning
2019-04-22 v1 Neural and Evolutionary Computing
Machine Learning
Abstract
This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultant optimal vector can be used as the initial centroids for K-means.
Keywords
Cite
@article{arxiv.1904.09098,
title = {Optimal initialization of K-means using Particle Swarm Optimization},
author = {Ashutosh Mahesh Pednekar},
journal= {arXiv preprint arXiv:1904.09098},
year = {2019}
}