  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
