Abstract

We present two enhanced visualization techniques for the self-organizing map allowing the intuitive representation of input data similarity. The general idea of both approaches is to visualize the relationship of nodes to facilitate the detection of cluster boundaries without modifying the architecture or the basic training process of SOM. One approach mirrors the movement of weight vectors during the training process within a two-dimensional (virtual) output space, whereas the second results in a grid of connected nodes where the intensity of the connection mirrors the similarity of the underlying data items. Both approaches can be combined to allow improved analysis of the inherent structure of high-dimensional input data and an intuitive recognition of cluster boundaries without the necessity of substantial prior knowledge concerning the input patterns. 1 Introduction The self-organizing map allows the mapping of high-dimensional input data onto a two-dimensional output space while...

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