Uncovering hierarchical structure in data using the growing hierarchical self-organizing map

Abstract

Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In particular, the representation of hierarchical relations and intuitively visible cluster boundaries are essential for a wide range of data mining applications. Current approaches based on neural networks hardly fulfill these requirements within a single model. In this paper we present the growing hierarchical self-organizing map ( GHSOM), a neural network model based on the self-organizing map. The main feature of this novel architecture is its capability of growing both in terms of map size as well as in a three-dimensional tree-structure in order to represent the hierarchical structure present in a data collection during an unsupervised training process. This capability, combined with the stability of the self-organizing map for high-dimensional feature space representation, makes it an ideal tool for data analysis and exploration. We demonstrate the potential of the GHSOM with an application from the information retrieval domain, which is prototypical both of the high-dimensional feature spaces frequently encountered in today's applications as well as of the hierarchical nature of data.