Visualization techniques provide attractive tools to explore and analyze huge and high dimensional gene expression sets. Several visualization techniques have been developed that enabled users to visually analyze high dimensional data. However, these techniques should be integrated with efficient exploration techniques, as efficient clustering, outlier analysis, ensembles and cluster validation to boost the quality of the analysis process. Integrating such techniques in an interactive framework exploits users' knowledge to further enhance the analysis process. In this work, an interactive visualization framework for gene expression analysis is proposed that introduces several features to the visualization process by integrating it with recent data analysis algorithms in an interactive framework.