Online clustering algorithms |
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Authors: | Barbakh Wesam Fyfe Colin |
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Institution: | The University of Paisley, Scotland, UK. wesam.barbakh@paisley.ac.uk |
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Abstract: | We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets. |
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