In measurement data there may exist outliers, data items lying very far from the main body of the data. The outliers may result, for instance, from measurement errors or typing errors made while inserting the statistics into a data base. In such cases it would be desirable that the outliers would not affect the result of the analysis. This is indeed the case for map displays generated by the SOM algorithm: each outlier affects only one map unit and its neighborhood, while the rest of the display may still be used for inspecting the rest of the data. Furthermore, the outliers can be easily detected based on the clustering display: the input space is, by definition, very sparsely populated near the outliers. If desired, the outliers can then be discarded and the analysis can be continued with the rest of the data set.
It is also possible that the outliers are not erroneous but that some data items really are strikingly different from the rest. In any case the map display reveals the outliers, whereby they can either be discarded or paid special attention to.