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Unformatted text preview: ensional topology
that preserves the neighborhood relations in the high dimensional data. Thus,
not only objects that are assigned to one cluster are similar to each other (as
in every cluster analysis), but also objects of nearby clusters are expected to be
more similar than objects in more distant clusters. Usually, two-dimensional
grids of squares or hexagons are used (cf. Fig. 3).
The network structure of a self-organizing map has two layers (see Fig. 3). The
neurons in the input layer correspond to the input dimensions, here the words Self Organizing Map (SOM, cf. Kohonen (1982)) 42 LDV-FORUM A Brief Survey of Text Mining
of the document vector. The output layer (map) contains as many neurons as
clusters needed. All neurons in the input layer are connected with all neurons
in the output layer. The weights of the connection between input and output
layer of the neural network encode positions in the high-dimensional data space
(similar to the cluster prototypes in k-means). Thus, every unit in the output
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.
- Summer '11