Gene expression data has a high application value for understanding the pathogenesis, disease diagnosis and gene-level drug development. However, the microarray data usually contains thousands of genes with a small number of samples, which causes serious curse of dimensionality and deteriorates the diagnosis accuracy. Moreover, it gives raise to difficulty to a lot of classifiers, and cuts down the cost of medical diagnosis. A new gene selection method is proposed, which is based on clustering and particle swarm optimization (PSO). Firstly, partition the genes using clustering algorithm and the useful are clusters selected for classification. Then the wrapper selection method based on particle swarm optimization(PSO） and extreme learning machine(ELM） is used to select the compact gene subset with high classification accuracy from the genes selected before. This method take advantages of clustering and PSO algorithm, and it can perform better in classification than other classical methods.