Often in the analysis of large scale gene expression data, it is desirable to group genes with similar expression profiles together. Many different algorithms and approaches exist to perform this task. However, each of them organize the data with differing abilities and sensitivities. We've constructed a comparative framework which allows the quantitative comparison of different clustering results. Using this framework, we've evaluated the performance of a variety of commonly used algorithms (K-Means, SOMs, and a bottom-up hierarchical Clustering) on data of varying structure to gain an understanding of the strengths and sensitivities of each of these different algorithms in the context of microarray data analysis.