Cartogram Data Projection for Self-Organizing Maps Enhanced visual data exploration in R

The Self-Organizing Map (SOM) is very often visualized by applying Ultsch’s Unified Distance Matrix (UMatrix) shading and labeling the cells of the 2-D grid with training data observations nearest to that node in feature space. Although powerful and the de facto standard visualization for SOMs, this does not provide for two key pieces of information when considering real world data mining applications: (a) While the U-Matrix indicates the location of possible clusters on the map, it typically does not accurately convey the size of the underlying data population within these clusters. (b) When mapping training data observations onto the 2-D grid of the SOM it often occurs that multiple observations are mapped onto a single cell of the grid. Simply labeling the observations on a single cell does not provide any insights of the feature-space distribution of observations within that cell and in practical data mining applications it is often desirable to understand the distribution or “goodness of fit” of the observations as they are mapped to the individual SOM