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

Abstract

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.