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Volume 14, Issue 3
Functional clustering with application to air quality analysis

Ming He, Hairong Li, Xiaoxin Zhu and Chunzheng Cao

J. Info. Comput. Sci. , 14 (2019), pp. 184-194.

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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.
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@Article{JICS-14-184, author = {Ming He, Hairong Li, Xiaoxin Zhu and Chunzheng Cao}, title = {Functional clustering with application to air quality analysis}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {14}, number = {3}, pages = {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. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22412.html} }
TY - JOUR T1 - Functional clustering with application to air quality analysis AU - Ming He, Hairong Li, Xiaoxin Zhu and Chunzheng Cao JO - Journal of Information and Computing Science VL - 3 SP - 184 EP - 194 PY - 2024 DA - 2024/01 SN - 14 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22412.html KW - air quality index, PM2.5 concentration, functional clustering, priori information AB - 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.
Ming He, Hairong Li, Xiaoxin Zhu and Chunzheng Cao. (2024). Functional clustering with application to air quality analysis. Journal of Information and Computing Science. 14 (3). 184-194. doi:
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