Cluster Ensemble for Gene Expression Microarray Data: Accuracy and Diversity

The classification of different types of cancer, historically, depended on efforts by the biologists that tried to establish, based on assumptions, the subtypes of a given tumor. However, up to now, there is no well-grounded methodology that aids to deal with such a task. One step towards this has arisen with the idea of analyzing the gene expression of tumors and basing the decision on such an analysis. In this context, we analyze the potential of applying cluster ensemble techniques to gene expression microarray data. Our experimental results show that there is often a significant improvement in the results obtained with the use of ensemble when compared to those based on the clustering techniques used individually.