Data point overlapping frequently occurs in scatterplots, resulting in visual clutters to interfere visual analysis. Some overlapping removal algorithms have been proposed to remove data point overlapping completely, however, they have some common shortcomings, mainly including the increasing of canvas size, distortion of data distribution, and dissatisfaction of time consumption. This work proposes that the complete removal of data point overlapping is non-essential, while slight overlapping is acceptable in some data analytical scenarios. Therefore, an incomplete overlapping removal algorithm is designed for scatterplots. First, the algorithm generates virtual data points in the blank areas in a scatterplot by using a semi-random generation method. Second, the algorithm uses a Voronoi diagram to divide each data point into an irregular grid, and then moves data points to grid centers to reduce the rate of data point overlapping and maintain the natural contour of data distribution. At last, the algorithm iteratively runs the step of Voronoi meshing and data point moving until that the rate of data point overlapping reaches a preset threshold. A series of objective and subjective experiments are conducted to evaluate the performance of the proposed algorithm and reference algorithms. The results show that users can quickly and accurately accomplish visual analysis tasks, including data point selection and regional density estimation, in scatterplots with a slight data point overlapping. The results reflect that the proposed algorithm is superior to all of the reference algorithms in the objective and subjective indicators.