Hierarchical clustering is an unsupervised technique, which is a common approach to study protein and gene expression data. In clustering, the patterns of expression of different genes are grouped into distinct clusters, in which the genes in the same cluster are assumed potential to be functionally related or to be influenced by a common upstream factor. Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, the uncertainty in the results obtained is still bothersome. Experimental repetitions are generally performed to overcome the drawbacks of biological variability and technical variability. In this study, the author proposes repeated measurement to evaluate the stability of gene clusters. This paper aims to prove that the stability from the gene clusters, incorporated with repeated measurement, can be used for further analysis.