Learning to visualise high-dimensional data
Ahmad, Khurshid and Vrusias, Bogdan (2004) Learning to visualise high-dimensional data Proceedings of the 8th International Conference on Information Visualisation. pp. 507-512.
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.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Date :||14 July 2004|
|Additional Information :||Ahmad, K. and Vrusias, B. (2004). Learning to Visualise High-Dimensional Data. Proceedings of the 8th International Conference on Information Visualisation, 507-512.© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Depositing User :||Mr Adam Field|
|Date Deposited :||27 May 2010 14:09|
|Last Modified :||23 Sep 2013 18:28|
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