Unformatted text preview: enters. Self-organizing maps, as discussed above, are an
An Example: Visualization Using Self-Organizing Maps Band 20 – 2005 49 Hotho, Nürnberger, and Paaß
alternative approach which is frequently used in data analysis to cluster high
dimensional data. The resulting clusters are arranged in a low-dimensional
topology that preserves the neighborhood relations of the corresponding high
dimensional data vectors and thus not only objects that are assigned to one
cluster are similar to each other, but also objects of nearby clusters are expected
to be more similar than objects in more distant clusters.
Usually, two-dimensional arrangements of squares or hexagons are used for
the deﬁnition of the neighborhood relations. Although other topologies are
possible for self-organizing maps, two-dimensional maps have the advantage
of intuitive visualization and thus good exploration possibilities. In document
retrieval, self-organizing maps can be used to arrange documents based on their
similarity. This approach opens up several appealing navigation possibilities.
Most important, the surrounding grid cells of documents known to be interes...
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