Robust seed selection algorithm for k-means type algorithms
Computer Vision and Pattern Recognition
2012-02-09 v1 Computational Engineering, Finance, and Science
Abstract
Selection of initial seeds greatly affects the quality of the clusters and in k-means type algorithms. Most of the seed selection methods result different results in different independent runs. We propose a single, optimal, outlier insensitive seed selection algorithm for k-means type algorithms as extension to k-means++. The experimental results on synthetic, real and on microarray data sets demonstrated that effectiveness of the new algorithm in producing the clustering results
Keywords
Cite
@article{arxiv.1202.1585,
title = {Robust seed selection algorithm for k-means type algorithms},
author = {K. Karteeka Pavan and Allam Appa Rao and A. V. Dattatreya Rao and G. R. Sridhar},
journal= {arXiv preprint arXiv:1202.1585},
year = {2012}
}
Comments
17 pages, 5 tables, 9figures