The probabilistic growing cell structures algorithm

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.KeywordsPrior ProbabilityInput SpaceRecursive FormulaRobot ConfigurationDirichlet TessellationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.