Scatterplot is a popular technique for visualizing high-dimensional datasets by using linear and nonlinear dimension reduction methods. These methods map the original high-dimensional dataset onto scatterplot points directly by dimension reduction, and hence require a high computation cost. Despite many improvements in scatterplot visual effects, however, when the data volume is large, the data mapped onto scatterplot data points will overlap, resulting a low quality of visualization. In this paper, we propose a novel software tool that ensembles five integrated components for fast multiview visualization of high-dimensional datasets: sampling, dimension reduction, clustering, multiview collaborative analysis, and dimension re-arrangement. In our tool, while the sampling component reduces the sizes of the datasets applying the random sampling technique to gain a high visualization efficiency, dimension reduction reduces the dimensions of the datasets applying principal-component analysis to improve the visualization quality. Next, clustering discovers hidden information in the reduced dataset applying fuzzy c-mean clustering to display hidden patterns of the original datasets. Finally, multiview collaborative analysis enables users to analyse multidimensional datasets from different aspects at the same time by combining scatterplot and scatterplot matrices. To optimize the visualization effects, in the scatterplot matrices, we re-arrange their dimensions and adjust the positions of scatterplots so that similar scatterplot points are adjacent in positions. As the result, in comparison with the existing visualization tools that apply some of these techniques, our tool not only improves the efficiency of dimension reduction but also enhances the quality of visualization and enables more comprehensive analysis. We test our tool on different real datasets to demonstrate its effectiveness. The experimental results validate that our method is effective in both efficiency and quality of visualization.