In recent years, the explosively growing amount of data in numerous clustering tasks has attracted considerable interest in boosting the existing clustering algorithms to large datasets. In this paper, the mean approximation approach is discussed to improve a spectrum of partition-oriented density-based algorithms. This approach filters out the data objects in the crowded grids and approximates their influence to the rest by their gravity centers. Strategies on implementation issues as well as the error bound of the mean approximation are presented. Mean approximation leads to less memory usage and simplifies computational complexity with minor lose of the clustering accuracy. Results of exhaustive experiments reveal the promising performance of this approach.