The Probabilistic Growing Cell Structures Algorithm

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. 1 Introduction The growing cell structures (GCS) algorithm [1] is an adaptive k-means algorithm [3] that performs data clustering. Based on the earlier work of Kohonen [4], GCS is a self-organizing neural network model [2] that incrementally builds a Dirichlet or Voronoi tessellation of the input space, while it ..