A new algorithm for initial cluster centers in k-means algorithm

Authors:
Murat Erisoglu;Nazif Calis;Sadullah Sakallioglu
Affiliations:
Department of Statistics, Faculty of Science and Letters, Cukurova University, 01300 Adana, Turkey;Department of Statistics, Faculty of Science and Letters, Cukurova University, 01300 Adana, Turkey;Department of Statistics, Faculty of Science and Letters, Cukurova University, 01300 Adana, Turkey
Venue:
Pattern Recognition Letters
Year:
2011

Citing 4
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Abstract

Clustering is one of the widely used knowledge discovery techniques to reveal structures in a dataset that can be extremely useful to the analyst. In iterative clustering algorithms the procedure adopted for choosing initial cluster centers is extremely important as it has a direct impact on the formation of final clusters. Since clusters are separated groups in a feature space, it is desirable to select initial centers which are well separated. In this paper, we have proposed an algorithm to compute initial cluster centers for k-means algorithm. The algorithm is applied to several different datasets in different dimension for illustrative purposes. It is observed that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm.