AIRCC's International Journal of Computer Science and Information Technology

K. Karteeka Pavan ¹, Allam Appa Rao ², A. V. Dattatreya Rao ³, G. R. Sridhar ⁴

Affiliations
1 Department of Computer Applications, Rayapati Venkata Ranga Rao and Jagarlamudi Chadramouli College of Engineering, Guntur, IN
2 Jawaharlal Nehru Technological University, Kakinada, IN
3 Department of Statistics, Acharya Nagarjuna University, Guntur, IN
4 Endocrine and Diabetes Centre, Andhra Pradesh, IN

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.

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