Creation of Text Document Matrices and Visualization by Self-Organizing Map

In the paper, text mining and visualization by self-organizing map (SOM) are investigated. At first, textual information must be converted into numerical one. The results of text mining and visualization depend on the conversion. So, the influence of some control factors (the common word list and usage of the stemming algorithm) on text mining results, when a document dictionary is created, is investigated. A self-organizing map is used for text clustering and graphical representation (visualization). A comparative analysis is made where a dataset consists of scientific papers about the optimization, based on Pareto, simplex, and genetic algorithms. Two new measures are also proposed to estimate the SOM quality when the classified data are analyzed: distances between SOM cells, corresponding to data items assigned to the same class, and the distance between centers of SOM cells, corresponding to different classes. The quantization error is measured to estimate the SOM quality, too.