LabelSOM: on the labeling of self-organizing maps

| Self-organizing maps are a prominent unsuper-vised neural network model providing cluster analysis of high-dimensional input data. However, in spite of enhanced vi-sualization techniques for self-organizing maps, interpreting a trained map proves to be diicult because the features responsible for a speciic 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 speciic 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 eld of data mining providing an example from real world text mining.