This is the **topic map**. It places the topics from the all the trained models on a two-dimensional map that attempts to place similar topics close to each other.

The clusters and colors are determined automatically by an algorithm, and provide only a rough guide to groups of topics that have similar themes. The different axes also do not have any intrinsic meaning, but are often interpretable as representing historical or thematic dimensions in the underlying corpus.

Checking the **collision detection** checkbox will minimize overlap among the nodes but distort the underlying similarity relationships.

The nodes are scaled according to the number of topics in the corresponding model. You can control which models are included in the map by clicking on the numbers on the left to toggle the corresponding models off and on.

You may also enter words in the search box to have the isomap change shading to help you find related topics.

Clicking on any topic circle will take you to the Hypershelf with the top documents for that topic already selected.

The topic map displayed here uses the non-linear [**isomap**](https://en.wikipedia.org/wiki/Isomap) method for reducing the multi-dimensional topic space to two dimensions.
