Circle Packing: Difference between revisions

Revision as of 21:46, 23 April 2007

Authors

Hongan Wang
Guozhong Dai
Weixin Wang
Hui Wang

Short description

Circle packing is a method to visualize large hierarchical data using nested circles. Inspired by treemaps and Grokker, Wang et al. developed a layout algorithm for tree structures which gives a good overview for large data sets. Tangent circles represent the brother nodes at the same level. The different leves are shown by nested circles (2D) or nested cylinders (3D). An advantage of this algorithm is the good overview for large data sets and it is easy to make out groupings and structural relationships.

figures

Suitable Datatypes

Example and use cases

Dynamic queries allow users to formulate queries by adjusting graphical widgets, such as slider, and see the results immediately. By providing a graphical visualization of the database and search results. Users can easliy change the parameters for search. The interfaces were used to explore a real-estate database and find homes meeting specific search criteria.

Internal Links

Treemap

References

[ACM, 1992] ACM, I ACM Press New York, http://portal.acm.org/citation.cfm?id=1124851&coll=GUIDE&dl=GUIDE&CFID=20762537&CFTOKEN=32865372

@@ Line 7: / Line 7: @@
 == Short description ==
-Circle packing is a method to visualize large hierarchical data using nested circles. Tangent circles represent the brother nodes at the same level. The different leves are shown by nested circles (2D) or nested cylinders (3D). An advantage of this algorithm is the good overview for large data sets and it is easy to make out groupings and structural relationships.
+Circle packing is a method to visualize large hierarchical data using nested circles. Inspired by treemaps and Grokker, Wang et al. developed a layout algorithm for tree structures which gives a good overview for large data sets. Tangent circles represent the brother nodes at the same level. The different leves are shown by nested circles (2D) or nested cylinders (3D). An advantage of this algorithm is the good overview for large data sets and it is easy to make out groupings and structural relationships.
 == figures ==