Interactive Visualization and Navigation in Large Data Collections using the Hyperbolic Space

We propose the combination of two recently introduced methods for the interactive visual data mining of large collections of data. Both, Hyperbolic Multi-Dimensional Scaling (HMDS) and Hyperbolic Self-Organizing Maps (HSOM) employ the extraordinary advantages of the hyperbolic plane (H2): (i) the underlying space grows exponentially with its radius around each point - ideal for embedding high-dimensional (or hierarchical) data; (ii) the Poincar´e model of the IH2 exhibits a fish-eye perspective with a focus area and a context preserving surrounding; (iii) the mouse binding of focus-transfer allows intuitive interactive navigation. The HMDS approach extends multi-dimensional scaling and generates a spatial embedding of the data representing their dissimilarity structure as faithfully as possible. It is very suitable for interactive browsing of data object collections, but calls for batch precomputation for larger collection sizes. The HSOM is an extension of Kohonen’s Self-Organizi...

• Type: Conference
• Year: 2003
• Where: ICDM
• Authors: Jörg A. Walter, Jörg Ontrup, Daniel Wessling, Helge Ritter
• Keywords: Data Mining, Hyperbolic Multi-Dimensional Scaling, ICDM 2003, Multi-Dimensional Scaling, Self-Organizing Map