Abstract: The amount of gene expression data scale is increasing sharply with the rapid development of bio-informatics technology, which poses a serious challenge for traditional clustering algorithms. Density-based hierarchical clustering (DHC) can solve the problem of the nested class of gene expression data and has good robustness, but for handling huge amounts of data. Therefore, a density-based hierarchical clustering algorithm on MapReduce(DisDHC) was proposed. It partitioned data sets into smaller blocks, clustered each block using DHC in parallel, gathered the result for re-clustering, and produced all density centers of each cluster. The experiments on GAL dataset, Cell cycle dataset, and Serum dataset show that DisDHC reduces clustering time and achieves high performance.