Exploration of relationships from texts using self-organizing maps

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This thesis explored and visualized the relationships of documents data, based on the technique of self-organizing maps (SOM), a subtype of artificial neural network for visualizing high-dimensional data in low-dimensional views. The source data for this thesis are the full Extensible Markup Language (XML) texts of A Standard Corpus of Present Day Edited American English. The first step is transforming these XML files to produce a term-document matrix, including stop word removal, stemming, tf-idf (term frequency–inverse document frequency) weighting, global filtering; here rows of this matrix represent documents as n-dimensional vectors. Secondly, these vectors are clustered and visualized by SOM consisting of neurons, each neuron relatives to a set of documents with a certain number of same terms. Then a network has been constructed from SOM, with vertices set of neurons and documents, lines set of linkages between...