An improved restricted growth function genetic algorithm for the consensus clustering of retinal nerve fibre data

This paper describes an extension to the Restricted Growth Function grouping Genetic Algorithm applied to the Consensus Clustering of a retinal nerve fibre layer data-set. Consensus Clustering is an optimisation based method which combines the results of a number of data clustering methods, and is used when it is unknown which clustering method is expected to perform the best. Consensus Clustering has been shown to produce results which are better than the averaged results of the input methods, but could benefit from a more efficient optimisation method. A Restricted Growth Function grouping Genetic Algorithm is a new method of grouping a number of objects into mutually exclusive subsets based upon a fitness function. This method does not suffer from degeneracy, and thus could be applied to the Consensus Clustering problem more efficiently than Simulated Annealing, the current optimisation method. Within this paper it is shown that this type of Genetic Algorithm can indeed improve the...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Stephen Swift, Allan Tucker, Jason Crampton, David Garway-Heath
• Keywords: Consensus Clustering, GECCO 2007, Restricted Growth Functions, Retinal Nerve Fibre