An Automatic Clustering Technique for Optimal Clusters

Details An Automatic Clustering Technique for Optimal Clusters An Automatic Clustering Technique for Optimal Clusters

2011-09-06

arxiv.org/abs/1109.1068v1

International journal of Computer Sciene Engineering and Applications, Vol., No.4, 2011, pp 133-144

Computer Science

Computer Vision and Pattern Recognition

12 pages, 5 figures, 2 tables

Scientific paper

10.5121/ijcsea.2011.1412

This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.

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