RLDEAO optimized of air quality data clustering analysis(RLDEAO优化的空气质量数据聚类分析)

RLDEAO optimized of air quality data clustering analysis(RLDEAO优化的空气质量数据聚类分析)

Aiming at the problems of high randomness, low clustering accuracy and multiple central points in clustering the same air quality data by traditional clustering methods due to different choice of initial points, a K-means complementary iterative air quality data clustering analysis method optimized...

Full description

Saved in:
Bibliographic Details
Main Authors: 田闯(TIAN Chuang), 黄鹤(HUANG He), 杨澜(YANG Lan), 王会峰(WANG Huifeng), 茹锋(RU Feng)
Format: Article
Language:zho
Published: Zhejiang University Press 2024-09-01
Series:Zhejiang Daxue xuebao. Lixue ban
Subjects:
k-means clustering algorithm(k-means聚类算法)
aquila optimizer (ao)(天鹰优化器（ao）)
weighted maximum minimum distance method(加权最大最小距离积法)
Online Access:https://doi.org/10.3785/j.issn.1008-9497.2024.05.003
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.3785/j.issn.1008-9497.2024.05.003

Similar Items