To tackle the failure of traditional clustering algorithms in dealing with large-scale data, the paper proposes a density-based statistical merging algorithm for large data sets (DSML). The algorithm takes each feature of data points as a set of independent random variable, and gets statistical merger criteria from the independent bounded difference inequality. To begin with, DSML improves Leaders algorithm by using the statistical merger criteria, and makes the improved algorithm as the sampling algorithm to obtain representative points. Secondly, combined with the density and the neighborhood information of representative points, the algorithm uses statistical merger criteria again to complete the clustering of the whole data set. Theoretical analysis and experimental results show that, DSML algorithm has nearly linear time complexity, can handle arbitrary data sets, and is insensitive to noise data. This fully proves the validity of DSML algorithm for large data sets.