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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Barbakh, W., Crowe, M., & Fyfe, C. (2006). A family of novel clustering algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 283–290). Springer Verlag. https://doi.org/10.1007/11875581_34
Mendeley helps you to discover research relevant for your work.