Classification Visualization across Mapping on a Sphere

Existing classification schemes are visualized as hierarchical trees. Science data visualization requires a new method in information space modelling in order to reveal relations between class nodes. This paper describes a novel visualization concept of classification scheme using subject content metrics. We have mapped the document collection of Association for Computing Machinery (ACM) digital library to a sphere surface. To overcome the incorrectness of linear measures in indexes distances we calculated similarity matrix of themes and multidimensional scaling coordinates. The results show that space distances between class nodes accurately correspond with the thematic proximities. Documents mapped into a sphere surface were located according to the classification nodes and distributed uniformly. Proposed method to visualize classification scheme is proper to reach nonlinearity in subject content visualization. This property allows us to place close by more classification nodes. Symmetry of a sphere favours a new subclasses and sublevels of classification trees uniform visualization. This method may be useful in the visual analysis of Computer Science and Engineering domain development being grown instantly.