Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm

The mental health of young college students has always been a social concern. Strengthening the supervision of college students’ mental health problems is an important research content. In this regard, this paper proposes to apply fuzzy cluster analysis to the health analysis of college students and...

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Bibliographic Details
Published in:Security and communication networks Vol. 2021; pp. 1 - 8
Main Authors: Tang, Qinghua, Zhao, Yixuan, Wei, Yujia, Jiang, Lu
Format: Journal Article
Language:English
Published: London Hindawi 03.09.2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Chebyshev approximation
Classification
Cluster analysis
Clustering
College students
Colleges & universities
Fuzzy sets
Gaussian process
Global optimization
Health education
Heuristic methods
Mental health
Personality
Perturbation
Population distribution
Quality of education
Questionnaires
Robustness (mathematics)
Search process
Students
ISSN:1939-0114, 1939-0122
Online Access:Get full text
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Summary:The mental health of young college students has always been a social concern. Strengthening the supervision of college students’ mental health problems is an important research content. In this regard, this paper proposes to apply fuzzy cluster analysis to the health analysis of college students and explore college students through fuzzy clustering. Explore the potential relationship between the factors that affect the health of college students, and this will provide a reference for the early prevention and intervention of college students’ mental health problems. In view of this, an improved fuzzy clustering method based on the firefly algorithm is proposed. First, the Chebyshev diagram is introduced into the firefly algorithm to initialize the population distribution. Then, an adaptive step size method is proposed to balance exploration and development capabilities. Finally, in the local search process, a Gaussian perturbation strategy is added to the optimal individual in each iteration to make it jump out of the local optimal. The process has good optimization capabilities and is easy to obtain the global optimal value. It can be used as the initial center of the fuzzy C-means clustering algorithm for clustering, which can effectively enhance the robustness of the algorithm and improve the global optimization ability. In order to evaluate the effectiveness of the algorithm, comparative experiments were carried out on four datasets, and the experimental results show that the algorithm is better than the comparison algorithm in clustering accuracy and robustness.
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ISSN:1939-0114
1939-0122
DOI:10.1155/2021/3960559