A Family of Novel Clustering Algorithms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


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.


Cluster Algorithm Performance Function Minimum Performance Fixed Point Algorithm Gaussian Basis Function 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Applied Computational Intelligence Research UnitThe University of PaisleyScotland

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