Visualization of Subtopics of the Thematic Document Collection Using the Context-Semantic Graph

An approach for visualization of nested topics within large collections of documents is proposed. The approach is based on set of parameters: information entropy, Kullback-Leibler divergence, Ginzburg algorithm, similarity the distributions of keywords and key phrases in the documents with Bernoulli´s theoretical distributions. The results of comparisons of our approach with implementations based on TF-IDF approaches are presented.