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On interactive visualization of high-dimensional data using the hyperbolic plane
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Edmonton, Alberta, Canada
SESSION: Visualization table of contents
Pages: 123 - 132  
Year of Publication: 2002
ISBN:1-58113-567-X
Authors
Jörg A. Walter  University of Bielefeld, D-33615 Bielefeld, Germany
Helge Ritter  University of Bielefeld, D-33615 Bielefeld, Germany
Sponsors
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
: AAAI
Publisher
ACM  New York, NY, USA
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ABSTRACT

We propose a novel projection based visualization method for high-dimensional datasets by combining concepts from MDS and the geometry of the hyperbolic spaces. Our approach Hyperbolic Multi-Dimensional Scaling (H-MDS) extends earlier work [7] using hyperbolic spaces for visualization of tree structures data ( "hyperbolic tree browser" ).By borrowing concepts from multi-dimensional scaling we map proximity data directly into the 2-dimensional hyperbolic space (H2). This removes the restriction to "quasihierarchical", graph-based data -- limiting previous work. Since a suitable distance function can convert all kinds of data to proximity (or distance-based) data this type of data can be considered the most general.We used the circular Poincaré model of the H2 which allows effective human-computer interaction: by moving the "focus" via mouse the user can navigate in the data without loosing the "context". In H2 the "fish-eye" behavior originates not simply by a non-linear view transformation but rather by extraordinary, non-Euclidean properties of the H2. Especially, the exponential growth of length and area of the underlying space makes the H2 a prime target for mapping hierarchical and (now also) high-dimensional data.We present several high-dimensional mapping examples including synthetic and real world data and a successful application for unstructured text. By analyzing and integrating multiple film critiques from news:rec.art.movies.reviews and the internet movie database, each movie becomes placed within the H2. Here the idea is, that related films share more words in their reviews than unrelated. Their semantic proximity leads to a closer arrangement. The result is a kind of high-level content structured display allowing the user to explore the "space of movies".


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Trevor F. Cox and Micheal A. Cox. Multidimensional Scaling. Monographs on Statistics and Appied Probability. Chapman and Hall, 1994.
 
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H.S.M. Coxeter. Non-Euclidean Geometry. University of Toronto Press, 1957.
 
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J.A. Hartigan. Representation of similarity matrices by trees. J. Am. Statist, Ass., 62:1140--1158, 1967.
 
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Frank Morgan. Riemannian Geometry: A Beginner's Guide. Jones and Bartlett Publishers, 1993.
 
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H. Ritter. Self-organizing maps on non-euclidean spaces. In S. Oja, E. & Kaski, editor, Kohonen Maps, pages 97--110. Elsevier, Amsterdam, 1999.
 
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J. W. Sammon, Jr. A non-linear mapping for data structure analysis. IEEE Transactions on Computers, 18:401--409, 1969.
 
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J.A. Thorpe. Elementary Topics in Differential Geometry. Springer Verlag, 1979.
 
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William P. Thurston and Jeffrey R. Weeks. The mathematics of three-dimensional manifolds. Scientific American, July:94--107, 1984.


Collaborative Colleagues:
Jörg A. Walter: colleagues
Helge Ritter: colleagues