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An investigation into unsupervised clustering techniques

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2 Author(s)
H. S. Lee ; Dept. of Electr. & Comput. Eng., Mississippi State Univ., MS, USA ; N. H. Younan

The performance of several unsupervised clustering techniques is compared using two clearly separated 3-D data sets that are not separable by any hyperplane. The result shows that the self-organizing feature map can cluster data sets successfully without any prior information of given data while the k-means and the fuzzy k-means algorithm fail to cluster correctly

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Southeastcon 2000. Proceedings of the IEEE

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