Abstract
An evolutionary rough c-means clustering algorithm is proposed. Genetic algorithms are employed to tune the threshold, and relative importance of upper and lower approximations of the rough sets modeling the clusters. The Davies–Bouldin clustering validity index is used as the fitness function, that is minimized while arriving at an optimal partitioning. A comparative study of its performance is made with related partitive algorithms. The effectiveness of the algorithm is demonstrated on real and synthetic datasets, including microarray gene expression data from Bioinformatics.
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