Applications of the growing self-organizing map

Abstract

The growing self-organizing map (GSOM), an extension of Kohonen’s self-organizing map algorithm, adapts not only the position of the map weight vectors in the input space, but also the topology of the map output space grid. This additional feature allows for an unsupervised generation of dimension-reducing projections with optimal neighborhood preservation, even if the effective dimensionality of the input data set is not known. In three case studies involving real-world data sets we show that the GSOM is able to reproducably generate projections with a very good degree of neighborhood preservation. For one of the data sets, an experimentally obtained time series from a nonlinear system, the correct dimensionality d A ≈3 of the underlying attractor is known from other methods; here the GSOM leads to maps without space grids which are also three dimensional.