A family of novel clustering algorithms

Authors:
Wesam Barbakh;Malcolm Crowe;Colin Fyfe
Affiliations:
Applied Computational Intelligence Research Unit, The University of Paisley, Scotland;Applied Computational Intelligence Research Unit, The University of Paisley, Scotland;Applied Computational Intelligence Research Unit, The University of Paisley, Scotland
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

We review the performance function associated with the familiar K-Means algorithm and that of the recently developed K-Harmonic Means. The inadequacies in these algorithms leads us to investigate a family of performance functions which exhibit superior clustering on a variety of data sets over a number of different initial conditions. In each case, we derive a fixed point algorithm for convergence by finding the fixed point of the first derivative of the performance function. We give illustrative results on a variety of data sets. We show how one of the algorithms may be extended to create a new topology-preserving mapping.