SOMA uses longitudinal data collected from the Ophthalmology Clinic of the Royal Liverpool University Hospital. Using trend mining (an extension of association rule mining) SOMA links attributes from the data. However the large volume of information at the output makes them difficult to be explored by experts. This paper presents the extension of the SOMA framework which aims to improve the post-processing of the results from experts using a visualisation tool which parse and visualizes the results, which are stored into XML structured files.
|Title of host publication
|2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 20 Oct 2016
|22nd International Conference on Automation and Computing - Colchester, United Kingdom
Duration: 7 Sep 2016 → 8 Sep 2016
Conference number: 22
|22nd International Conference on Automation and Computing
|7/09/16 → 8/09/16