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|Title||Clustering with alternative similarity functions|
We [6, 7] have recently investigated several families of clustering algorithms. In this paper, we show how a novel similarity function can be integrated into one of our algorithms as a method of performing clustering and show that the resulting method is superior to existing methods in that it canbe shown to reliably find a globally optimal clustering rather than local optima which other methods often find. We also extend the method to perform topology preserving mappings and show the results of such mappings on artificial and real data.
|Published in||Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases|
|Publisher||World Scientific and Engineering Academy and Society (WSEAS)|
|Item link||Item Link|
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|Ashour, Wesam M._29.pdf||587.8Kb|