A novel framework to elucidate core classes in a dataset

Soria, Daniele and Garibaldi, Jonathan M. (2010) A novel framework to elucidate core classes in a dataset. In: IEEE Congress on Evolutionary Computation (CEC) 2010, 18-23 July 2010, Barcelona, Spain.

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In this paper we present an original framework to extract representative groups from a dataset, and we validate it

over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visualisation techniques are used to characterise the results, and core classes are defined by consensus clustering. Classes may be verified using supervised classification algorithms to obtain a set of rules which may be useful for new data points in the future. This framework is validated over a novel set of histone markers for breast cancer patients. From a technical perspective, the resultant classes are well separated and characterised by low, medium and high levels of biological markers. Clinically, the groups appear to distinguish patients with poor overall survival from those with low grading score and better survival. Overall, this framework offers a promising methodology for elucidating core consensus groups from data.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: IEEE Congress on Evolutionary Computation (CEC) 2010, IEEE, 2010, ISBN 978-1-4244-8126-2, pp. 1-8 doi: 10.1109/CEC.2010.5586331
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Soria, Dr Daniele
Date Deposited: 26 Feb 2015 14:41
Last Modified: 14 Oct 2017 11:41
URI: http://eprints.nottingham.ac.uk/id/eprint/28139

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