Thus every unit in the output layer represents a

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

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 layer represents...
View Full Document

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.

Ask a homework question - tutors are online