Analysis of high-dimensional data using local input space histograms

The idea of local input space histograms was recently introduced as a means to augment prototype-based vector quantization methods in order to gather more information about the structure of the respective input space. Here we investigate the utility of this new idea for analysing and clustering high-dimensional data. Our results demonstrate that the additional information gained about the input space structure can be used to enable and improve visualization and hierarchical clustering. Furthermore, we show that contrary to common view the Minkowski distance with p>1 can be a meaningful distance measure for high-dimensional data.