Robust seed selection algorithm for k-means type algorithms
  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 
K. Karteeka Pavan, Allam Appa Rao, A.V. Dattatreya Rao, G.R.Sridhar
cs.CV cs.CE