Laying out and visualizing large trees using a hyperbolic space
UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
An evaluation of space-filling information visualizations for depicting hierarchical structures
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
An Automated Classification System and Associated Digital Library Services
NDDL '01 Proceedings of the 1st International Workshop on New Developments in Digital Libraries: n conjunction with ICEIS 2001
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Information Visualization: Beyond the Horizon
Information Visualization: Beyond the Horizon
Self organization of a massive document collection
IEEE Transactions on Neural Networks
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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.