A fast algorithm for generating set partitions
The Computer Journal
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Analysis of Algorithms for Listing Equivalence Classes of k-ary Strings
SIAM Journal on Discrete Mathematics
ACM Computing Surveys (CSUR)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms
Evolutionary Computation
Practical Statistics for Medical Research
Practical Statistics for Medical Research
Genetic algorithms, path relinking, and the flowshop sequencing problem
Evolutionary Computation
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
Variable grouping in multivariate time series via correlation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The effect of cooling functions on ensemble clustering using simulated annealing
Intelligent Data Analysis
| Hi-index | 0.00 |
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.