Robust seed selection algorithm for k-means type algorithms
This work addresses the issue of unreliable clustering outcomes for users of k-means algorithms, though it appears incremental as an extension to k-means++.
The authors tackled the problem of inconsistent clustering results due to varying initial seed selection in k-means algorithms by proposing a robust, outlier-insensitive seed selection algorithm as an extension to k-means++. The experimental results on synthetic, real, and microarray datasets demonstrated its effectiveness in producing clustering results.
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