Clustering with alternative similarity functions
Wesam Ashour Barbakh, Colin Fyfe · International Conference on Artificial Intelligence · 2008
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.