Аннотация
The growing self-organizing map (GSOM) algorithm is
presented in detail and the effect of a spread factor,
which can be used to measure and control the spread of
the GSOM, is investigated. The spread factor is
independent of the dimensionality of the data and as
such can be used as a controlling measure for
generating maps with different dimensionality, which
can then be compared and analyzed with better accuracy.
The spread factor is also presented as a method of
achieving hierarchical clustering of a data set with
the GSOM. Such hierarchical clustering allows the data
analyst to identify significant and interesting
clusters at a higher level of the hierarchy, and
continue with finer clustering of the interesting
clusters only. Therefore, only a small map is created
in the beginning with a low spread factor, which can be
generated for even a very large data set. Further
analysis is conducted on selected sections of the data
and of smaller volume. Therefore, this method
facilitates the analysis of even very large data sets
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