Combined unsupervised biclustering of microarray data

Clustering techniques play an important role in analyzing high dimensional data such as microarray data. In this case, the clustering methods identify groups of genes that manifest similar expression patterns and are activated by similar conditions. In this paper, we combined k-means algorithm with Partitioning Around Medoids (PAM) and Expectation-Maximization (EM) in order to obtained an optimal biclustering of microarray datasets. Internal and external validation methods were used before clustering.