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Clustering methods have been often applied to large data with the main purpose of reducing the dimension, time computation and identifying clusters with similar behavior. This work presents a state-of-the-art in unsupervised clustering and cluster validation. It proposes a method for hybrid bi-clustering of microarray data combined with a supervised validation for determining the optimal amount of clusters of genes.doi:10.11601/ijates.v2i3.21 fatcat:uvuizkmb4nfm7k62v3cmaif34m