qik is a probability vector k1 qic 1 c the underlying

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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.

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