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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A Self Acting Initial Seed Selection Algorithm for K-means Clustering Based on Convex-Hull
verfasst von : S. M. Shahnewaz, Md. Asikur Rahman, Hasan Mahmud
Erschienen in: Informatics Engineering and Information Science
Verlag: Springer Berlin Heidelberg
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The classic k-means algorithm and its variations are sensitive to the choice of starting points and always get stuck at local optimal values. In this paper, we have presented a self-acting initial seed selection algorithm for kmeans clustering which estimates the density information of input points based on the theory of convex-hulls. To reach into the core of actual clusters, we successively exploit the convex-hull vertices of given input set to construct new intermediate cluster centres. We also introduce a cluster merging technique which amalgamates the similar clusters to avoid getting stuck at local optimal values. Results of numerical experiments on synthetic and benchmark (
iris
and
ruspini
) datasets demonstrate that proposed algorithm is more efficient in terms of number of true cluster, purity and normalized information gain than the classic k-means algorithm. Thus, the feasibility of our algorithm in two dimensional space was validated.