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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Barbakh, W., Crowe, M., Fyfe, C. (2006). A Family of Novel Clustering Algorithms. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_34
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DOI: https://doi.org/10.1007/11875581_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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