@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 = {2019},
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 = {3080-180X},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22412.html}
}