Methods for Interpreting a Self-Organized Map in Data Analysis
Samuel Kaski, Janne Nikkilä, Teuvo Kohonen · 1998
. The Self-Organizing Map (SOM) can be used for forming overviews of multivariate data sets and for visualizing them on graphical map displays. Each map location represents certain kinds of data items and the value of a variable in the representations can be visualized in the corresponding locations on the map display. Such component plane displays contain all the information needed for interpreting the map but information about the relations of the variables remains implicit. We have developed methods that visualize explicitly the contribution of each variable in the organization of the map at different locations. It is also possible to measure the contribution of each variable in the cluster structure within an area of the map to summarize, for instance, the characteristics of clusters. 1. Introduction The SOM algorithm [2, 3] forms a mapping of a usually two-dimensional map lattice into the high-dimensional data space. There is a model vector connected to each point of the discrete...