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 performance of Consensus Clustering, and in fact can be improved further by taking advantage of some application specific properties. These findings are demonstrated on a retinal nerve fibre layer data-set and on a synthetic data-set.
restricted growth functions genetic algorithms consensus clustering grouping retinal nerve fibre layer