Competitive learning methods can also be used for density estimation, i.e. for the generation of an estimate for the unknown probability density of the input signals.
Another possible goal is clustering, where a partition of the data into subgroups or clusters is sought, such that the distance of data items within the same cluster (intra-cluster variance) is small and the distance of data items stemming from different clusters (inter-cluster variance) is large. Many different flavors of the clustering problem exist depending, e.g., on whether the number of clusters is pre-defined or should be a result of the clustering process. A comprehensive overview of clustering methods is given by Jain and Dubes (1988).
Combinations of competitive learning methods with supervised learning approaches are feasible, too. One possibility are radial basis function networks (RBFN) where competitive learning is used to position the radial centers (Fritzke, 1994b; Moody and Darken, 1989). Moreover, local linear maps have been combined with competitive learning methods (Fritzke, 1995b; Martinetz et al., 1989, 1993; Walter et al., 1990). In the simplest case for each Voronoi region one linear model is used to describe the input/output relationship of the data within the Voronoi region.