User:Garycmartin: Difference between revisions

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== Kohonen SOM Visualisations ==

These Self Organising Maps (SOMs) acts as 2D ~~spacial~~ summariser visualisations of (multidimensional) text distance metrics generated from the weekly content on its.an.education.project. Using a geographic like landscape metaphor for visualisation, height (colour gradient) indicates features with strong associations; proximity represents association between features (i.e related words), and label size is a rough guide to basic frequency of the feature. There are many "correct" map layouts for the same set of data, each generation will usually settle into a slightly different set of local minima, but the associations are no less valid for each. After removing linguistic junk words, and word stemming, the map is currently picking the top ~200 features by frequency. The map is continuous and wraps around north/south and east/west (surface of a torus).

These Self Organising Maps (SOMs) acts as 2D spatial summariser visualisations of (multidimensional) text distance metrics generated from the weekly content on its.an.education.project. Using a geographic like landscape metaphor for visualisation, height (colour gradient) indicates features with strong associations; proximity represents association between features (i.e related words), and label size is a rough guide to basic frequency of the feature. There are many "correct" map layouts for the same set of data, each generation will usually settle into a slightly different set of local minima, but the associations are no less valid for each. After removing linguistic junk words, and word stemming, the map is currently picking the top ~200 features by frequency. The map is continuous and wraps around north/south and east/west (surface of a torus).

[[Image:SOM_legend.jpg|172px]]

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 == Kohonen SOM Visualisations ==
-These Self Organising Maps (SOMs) acts as 2D spacial summariser visualisations of (multidimensional) text distance metrics generated from the weekly content on its.an.education.project. Using a geographic like landscape metaphor for visualisation,  height (colour gradient) indicates features with strong associations; proximity represents association between features (i.e related words), and label size is a rough guide to basic frequency of the feature. There are many "correct" map layouts for the same set of data, each generation will usually settle into a slightly different set of local minima, but the associations are no less valid for each. After removing linguistic junk words, and word stemming, the map is currently picking the top ~200 features by frequency. The map is continuous and wraps around north/south and east/west (surface of a torus).
+These Self Organising Maps (SOMs) acts as 2D spatial summariser visualisations of (multidimensional) text distance metrics generated from the weekly content on its.an.education.project. Using a geographic like landscape metaphor for visualisation,  height (colour gradient) indicates features with strong associations; proximity represents association between features (i.e related words), and label size is a rough guide to basic frequency of the feature. There are many "correct" map layouts for the same set of data, each generation will usually settle into a slightly different set of local minima, but the associations are no less valid for each. After removing linguistic junk words, and word stemming, the map is currently picking the top ~200 features by frequency. The map is continuous and wraps around north/south and east/west (surface of a torus).
 [[Image:SOM_legend.jpg|172px]]