Adoption of K-means clustering algorithm in smart city security analysis and mythical experience analysis of urban image
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| Publicado no: | PLoS One vol. 20, no. 3 (Mar 2025), p. e0319620 |
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| Autor principal: | |
| Publicado em: |
Public Library of Science
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| Acesso em linha: | Citation/Abstract Full Text Full Text - PDF |
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| Resumo: | ObjectiveAn 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.MethodsAn 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.ConclusionThe 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. |
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| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0319620 |
| Fonte: | Health & Medical Collection |