Abstract
By applying different clustering algorithms, the author strived to construct the best visual representation of scientific domains and disciplines in Poland. Journals and their disciplinary categories constituted a data set. A comparative analysis of maps was based on both qualitative and quantitative approaches. Complex patterns of eight maps were evaluated taking into account both the local proximity of disciplines and the whole structure of presented domains. Final clustering quality value was introduced and calculated in reference to the knowledge domains. The authors underlined the role of quantitative and qualitative methods in combination in the mapping evaluation. The best results were obtained with the T-distributed stochastic neighbour embedding (t-SNE) algorithm. This youngest technique may have the biggest potential for semantic information studies and in the scope of broadly understood semantic solutions.
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