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Robust Seed Selection Algorithm for K-Means Type Algorithms


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

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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  • Robust Seed Selection Algorithm for K-Means Type Algorithms

Abstract Views: 345  |  PDF Views: 182

Authors

K. Karteeka Pavan
Department of Computer Applications, Rayapati Venkata Ranga Rao and Jagarlamudi Chadramouli College of Engineering, Guntur, India
Allam Appa Rao
Jawaharlal Nehru Technological University, Kakinada, India
A. V. Dattatreya Rao
Department of Statistics, Acharya Nagarjuna University, Guntur, India
G. R. Sridhar
Endocrine and Diabetes Centre, Andhra Pradesh, India

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.