Genetic algorithm guided clustering

Genetic algorithms provide an approach to optimization. Unsupervised clustering algorithms attempt to optimize the placement of like objects into homogeneous classes or clusters. We describe an approach to using genetic algorithms to optimize the clusters created during unsupervised clustering. Hard partitions of the feature space are the members of the population. They evolve into better partitions based upon the fitness function which is a version of the hard c-means optimization function. The methods of crossover and mutation are described. An example of the clustering performance of this approach is shown with the Iris data. The genetic guided clustering is shown to outperform hard c-means on the Iris data in terms of the number of patterns which are correctly placed into a partition whose majority class is the same as the assigned pattern.<
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