Robust seed selection algorithm for k-means type algorithms

K. Karteeka Pavan, Allam Appa Rao, A. V. Dattatreya Rao, G. R. Sridhar

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

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