Performance Comparisons of PSO based Clustering
Neural and Evolutionary Computing
2016-09-08 v1 Machine Learning
Abstract
In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form the basis of comparison of PSO based approaches. We have explored different variants of PSO such as gbest, lbest ring, lbest vonneumann and Hybrid PSO for comparison purposes. The results reveal that PSO based clustering algorithms perform better compared to K means in all data sets.
Cite
@article{arxiv.1001.5348,
title = {Performance Comparisons of PSO based Clustering},
author = {Suresh Chandra Satapathy and Gunanidhi Pradhan and Sabyasachi Pattnaik and J. V. R. Murthy and P. V. G. D. Prasad Reddy},
journal= {arXiv preprint arXiv:1001.5348},
year = {2016}
}
Comments
6 pages 2 figures