LabelSOM: on the labeling of self-organizing maps

Self-organizing maps are a prominent unsupervised neural network model providing cluster analysis of high-dimensional input data. However, in spite of enhanced visualization techniques for self-organizing maps, interpreting a trained map proves to be difficult because the features responsible for a specific cluster assignment are not evident from the resulting map representation. In this paper we present our LabelSOM approach for automatically labeling a trained self-organizing map with the features of the input data that are the most relevant ones for the assignment of a set of input data to a particular cluster. The resulting labeled map allows the user to understand the structure and the information available in the map and the reason for a specific map organization, especially when only little prior information on the data set and its characteristics is available. We demonstrate the applicability of the LabelSOM method in the field of data mining providing an example from real world text mining.