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
A focus+context technique based on hyperbolic geometry for visualizing large hierarchies
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The ecological approach to text visualization
Journal of the American Society for Information Science - Speical issue on integrating mutiple overlapping metadata standards
An initial examination of ease of use for 2D and 3D information visualizations of Web content
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Visual information foraging in a focus + context visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Self-Organizing Maps
On interactive visualization of high-dimensional data using the hyperbolic plane
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
H3: laying out large directed graphs in 3D hyperbolic space
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Information Systems - Knowledge discovery and data mining (KDD 2002)
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A cartographic approach to visualizing conference abstracts
IEEE Computer Graphics and Applications
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
International Journal of Approximate Reasoning
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We propose the combination of two recently introducedmethods for the interactive visual data mining of largecollections of data. Both, Hyperbolic Multi-DimensionalScaling (HMDS) and Hyperbolic Self-Organizing Maps(HSOM) employ the extraordinary advantages of the hyperbolicplane (H2): (i) the underlying space grows exponentiallywith its radius around each point - ideal for embeddinghigh-dimensional (or hierarchical) data; (ii) thePoincaré model of the IH2 exhibits a fish-eye perspectivewith a focus area and a context preserving surrounding; (iii)the mouse binding of focus-transfer allows intuitive interactivenavigation.The HMDS approach extends multi-dimensional scalingand generates a spatial embedding of the data representingtheir dissimilarity structure as faithfully as possible. Itis very suitable for interactive browsing of data object collections,but calls for batch precomputation for larger collectionsizes.The HSOM is an extension of Kohonen's Self-OrganizingMap and generates a partitioning of the data collection assignedto an IH2 tessellating grid. While the algorithm'scomplexity is linear in the collection size, the data browsingis rigidly bound to the underlying grid.By integrating the two approaches we gain the synergetic effectof adding advantages of both. And the hybrid architectureuses consistently the IH2 visualization and navigationconcept. We present the successfully application to a textmining example involving the Reuters-21578 text corpus.