Learning to Visualise High-Dimensional Data

Visualisation techniques focus on reducing high dimensional data to a low dimensional surface or a cube. Similar dimensional reduction is attempted in the so-called ‘self-organising maps’. A number of techniques have been developed to visualise categories learnt by these maps through and exemplified by the term sequential clustering. An evaluation of the techniques is presented using the learning capability of the self-organising maps as a baseline for building systems that learn to visualise complex data. 1