Changes

Gary C. Martin (gary at garycmartin dot com) is a freelance software developer based in Edinburgh/UK, with a focus on design, UI/HCI, analytics and, information visualisation. His main contributions to the OLPC project so far are the [http://wiki.laptop.org/go/Moon Moon] activity, and a set of SVG toolbar icons for [http://wiki.laptop.org/go/Calculate Calculate]. His homepage is over at http://www.garycmartin.com/

Gary C. Martin (gary at garycmartin dot com) is a freelance software developer based in Edinburgh/UK, with a focus on design, UI/HCI, analytics and, information visualisation. His main contributions to the OLPC project so far are the [http://wiki.laptop.org/go/Moon Moon] activity, and a set of SVG toolbar icons for [http://wiki.laptop.org/go/Calculate Calculate]. His homepage is over at http://www.garycmartin.com/

== Kohonen SOM Visualisations ==

== IAEP Kohonen SOM Visualisations ==

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,  the height (colour gradient) indicates features with strong associations to all other features; proximity represents association between specific features (i.e related words), and label size is a rough guide to basic frequency of a 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 surface 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,  the height (colour gradient) indicates features with strong associations to all other features; proximity represents association between specific features (i.e related words), and label size is a rough guide to basic frequency of a 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 surface is continuous and wraps around north/south and east/west (surface of a torus).