With many genomes completed and extensive applications of DNA chips, analysis of the gene expression data has become a hotspot in the postgenomic age. Clustering is the art to group genes with related functions according to the similarities in their expression profiles. A number of clustering algorithms have been developed for gene expression data analysis. For their respective focuses and principles, every method has its own advantages and disadvantages, which are reviewed. How to evaluate the capabilities of these algorithms, and to develop new methods more suitable for gene expression analysis, should be urgent.