On-line trajectory classification

Sas, Corina and O'Hare, G. and Reilly, R. (2003) On-line trajectory classification. In: International Conference on Computational Science, Workshop on Scientific Visualisation and Human-Machine Interaction in a Problem-Solving Environment. Springer Verlag, pp. 1035-1044.

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Abstract

This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/q1
Subjects:
ID Code:
42339
Deposited By:
Deposited On:
11 Dec 2008 13:09
Refereed?:
No
Published?:
Published
Last Modified:
14 Aug 2020 06:36