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Unformatted text preview: structure in the highdimensional sample data is non-linearly projected to the lower-dimensional
topology. After learning, arbitrary vectors (i.e. vectors from the sample set
or prior ‘unknown’ vectors) can be propagated through the network and are
mapped to the output units. For further details on self-organizing maps see
Kohonen (1984). Examples for the application of SOMs for text mining can be
found in Lin et al. (1991); Honkela et al. (1996); Kohonen et al. (2000); Nürnberger
(2001); Roussinov & Chen (2001) and in Sect. 3.4.2. Clustering can also be viewed
from a statistical point of view. If we have k different clusters we may either
assign a document di with certainty to a cluster (hard clustering) or assign Model-based Clustering Using the EM-Algorithm Band 20 – 2005 43 Hotho, Nürnberger, and Paaß Figure 3: Network architecture of self-organizing maps (left ) and possible neighborhood
function v for increasing distances from s (right ) di with probability qic to Pc (soft clustering), where qi = (qi1 , ....
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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