An improved K-means algorithm and its application in the evaluation of air quality levels

This research introduces an improved k-means algorithm which combines hierarchical method with k-means method and the application in the evaluation of air quality levels. In present, evaluation of air quality relies on a tedious index computing based on a formula. A comprehensive analysis on single pollutant cannot be seen. The new clustering method gives more proper initial points calculated by hierarchical method and chooses a more precise distance computing method that makes the coefficients the smallest for association between every sample. It has a more proper clusters result as well as less iterations. This paper collects 392 days' air pollutants records and does the clustering by the new method. An evaluation including five levels indicates every day's pollutant features. This application will help researchers do analysis more convenient and give possibility for the automated of evaluation in the big data era.