The huge number of gene expressions resulting from a single microarray experiment, together with the large number of tumor samples, needs efficient methods that can extract hidden information and structure in such data sets. Clustering is a common analysis tool used to find groups of gene expression patterns. However, analysis of large clusters can be an infeasible task in large sets. In this work, a method is proposed to capture the main structure of the data by identifying core gene expressions. This reduces the data to only a subset of representatives used to grasp the main behavior of gene expression. When integrated with clustering, it becomes feasible to analyze clusters of large sizes, and to identify main expression patterns and relations between them. The importance of using a connected-based clustering is emphasized in order to reveal the gradual change between core gene expressions, something which cannot be achieved using traditional clustering algorithms. Analysis is done on breast cancer data to illustrate the significance of the proposed methodology.