Functional clustering with application to air quality analysis
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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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