This is a really cool 3D visualization of a tag cloud with distances:
http://langtech.jrc.ec.europa.eu/Pictures/ThemeScape-overview_EP259.pdf
What is the sequence to make this? I'm thinking:
1) Create a document/term matrix.
2) Random Projection of term vectors onto 2D.
2D distances match N-dimensional distances between terms.
3) Do SVD of term vectors.
4) Use first feature vector to select height of each term.
Or, norm of the feature vector X singular values.
After this, the mapping software does the rest of the work via topo
and word placement algorithms.
--
Lance Norskog
[email protected]
