**5. Conclusion**

In this chapter, we collected the data of air pollution stations in Korea and used Kmeans clustering to learn about data mining and machine learning algorithms. We divide air pollution areas to predict the distribution of air pollution using air pollution concentration clustering. The training dataset is latitude, longitude, NO2, SO2, CO, O3, PM10, PM25, with air pollution data for one month in April 2020. We use the collected dataset and classify air pollution monitoring stations. Based on the central coordinates of the cluster, the areas of the Korean territory were classified through the Voronoi algorithm. Finally, we confirmed that the proposed air pollution area could be classified by considering the distribution of air pollution, unlike traditional administrative districts. Moreover, the proposed area can help understand the distribution of air pollution in the shaded areas that do not have air pollution stations.
