Content area

Abstract

Objective

An information security evaluation model based on the K-Means Clustering (KMC) +  Decision Tree (DT) algorithm is constructed, aiming to assess its value in evaluating smart city (SC) security. Additionally, the impact of SCs on individuals’ mythical experiences is investigated.

Methods

An information security analysis model based on the combination of KMC and DT algorithms is established. A total of 38 SCs are selected as the research objects for practical analysis. The practical feasibility of the model is assessed using the receiver operating characteristic (ROC) curve, and its performance is compared with that of the Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) classification methods. Lastly, a questionnaire survey is conducted to obtain and analyze individuals’ mythical experiences in SCs.

Results

(1) The area under the ROC curve is significantly higher than 0.9 (0.921 vs. 0.9). (2) Compared to the NB and LR algorithms, the security analysis model based on the combination of KMC and DT algorithms demonstrated higher true positive rate (TPR), accuracy, recall, F-Score, AUC-ROC, and AUC-PR. Additionally, the performance metrics of RF, SVM, and GBM are similar to those of the KMC+DT model. (3) When the attributes are the same, the difference in smart risk levels is small, while when the attributes are different, the difference in risk levels is significant. (4) The support rates for various types of new folk activities are as follows: offline shopping festivals (17.6%), New Year’s Eve celebrations (16.7%), Tibet tourism (15.6%), spiritual practices (16.2%), green leisure (16.0%), and suburban/rural tourism (15.8%). (5) High-risk cities (Grade A) showed stronger support for modern activities such as offline shopping festivals and green leisure, while low-risk cities (Grades C and D) tended to favor traditional cultural activities.

Conclusion

The algorithm model constructed in this work is capable of effectively evaluating the information security risks of SCs and has practical value. A good city image and mythological experience are driving the development of cities.

Details

1009240
Business indexing term
Title
Adoption of K-means clustering algorithm in smart city security analysis and mythical experience analysis of urban image
Author
Publication title
PLoS One; San Francisco
Volume
20
Issue
3
First page
e0319620
Publication year
2025
Publication date
Mar 2025
Section
Research Article
Publisher
Public Library of Science
Place of publication
San Francisco
Country of publication
United States
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Milestone dates
2024-07-31 (Received); 2025-02-04 (Accepted); 2025-03-10 (Published)
ProQuest document ID
3175932515
Document URL
https://www.proquest.com/scholarly-journals/adoption-k-means-clustering-algorithm-smart-city/docview/3175932515/se-2?accountid=208611
Copyright
© 2025 Haotong Han. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-09-15
Database
ProQuest One Academic