Clustering algorithms are wildly used in the research of microarray data to extract groups of genes or samples that are tightly coexpressed. In most of them, some parameters should be predefined artificially, however, it is very difficult to determine them manually without prior domain knowledge. To handle this problem, an iterative clustering algorithm is proposed. Firstly, by sorting the original data by dominant set, similar genes would be aligned together. It’s hard to specify the cluster boundary. A criterion is presented to partition a cluster from the sorted data according to the property that the distances between the inside elements are smaller than that of outside elements. The idea is to remove the cluster form the current data set, repeat the process, and stop the algorithm when the stop criterions are satisfied. The new clustering algorithm is analyzed on several aspects and tested on the published yeast cell-cycle microarray data. The results of the application confirm that the method is very applicable, efficient and has good ability to resist noise.