Serendipity in Text and Audio Information Spaces: Organizing and Exploring High-Dimensional Data with the Growing Hierarchical Self-Organizing Map

The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are caused, on the one hand, by the static architecture of this model, as well as, on the other hand, by the limited capabilities for the representation of hierarchical relations of the data. With our Growing Hierarchical Self-Organizing Map we present an artificial neural network model with hierarchical architecture composed of independent growing self-organizing maps to address both limitations. The motivation is to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. The benefits of this neural network are first, a problem-dependent architecture, and second, the intuitive representation of hierarchical relations in the data. This is especially appealing in exploratory data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.