Abstract
The growing cell structures (GCS) algorithm is an adaptive k-means clustering algorithm in which new clusters are added dynamically to produce a Dirichlet tessellation of the input space. In this paper we extend the non-parametric model of the GCS into a probabilistic one, assuming that samples are distributed in each cluster according to a multi-variate normal probability density function. We show that by recursively estimating the means and the variances of the clusters, and by introducing a new criterion for the insertion and deletion of a cluster, our approach can be more powerful to the original GCS algorithm. We demonstrate our results within the mobile robots paradigm.
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© 1997 Springer-Verlag Berlin Heidelberg
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Vlassis, N.A., Dimopoulos, A., Papakonstantinou, G. (1997). The probabilistic growing cell structures algorithm. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020228
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DOI: https://doi.org/10.1007/BFb0020228
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