Functional clustering with application to air quality analysis
Year: 2019
Journal of Information and Computing Science, Vol. 14 (2019), Iss. 3 : pp. 184–194
Abstract
School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China (Received March 21 2019, accepted June 20 2019) Based on the air quality status of 161 cities in China, this paper studies the temporal and spatial distribution characteristics of PM2.5 concentration of major pollutants affecting air quality index (AQI). We use improved functional clustering analysis methods and add priori information about location and human factors to make the clustering results more accurate. The improved functional clustering model is compared with the basic sparse data function clustering method, k-centres functional clustering method, functional principal component analysis and traditional K-means clustering method by repeated simulation. Finally, we use the PM2.5 concentration of selected 161 cities in China as an illustrative example.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22412
Journal of Information and Computing Science, Vol. 14 (2019), Iss. 3 : pp. 184–194
Published online: 2019-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 11