基于混合聚类算法的大学生心理健康分析

Mental health analysis of college students based on hybrid clstering algorithm

为提高大学生心理健康认知水平,及时了解大学生心理健康状况,文中提出了由K-means和蚁狮优化算法混合的聚类分析算法,分析大学生心理健康情况,并将心理健康划分为正常、轻微、中等、严重和特别严重5个级别。仿真阶段以群内平均距离和F测度为指标将所提算法与传统K-means、K-meansPSO、K-meansFA、模糊K-means等进行对比,结果表明,算法整体性能优于其他方法。

In order to improve the cognitive level of College Students’mental health and timely understand their mental health status,this paper proposes a clustering analysis algorithm based on K-means and ant lion optimization algorithm to analyze the mental health of college students,and divides the mental health into five levels:normal,mild,moderate,severe and especially serious.In the simulation stage,taking the intra group average distance and F measure as indicators,the proposed algorithm is compared with traditional K-means,K-means PSO,K-means FA and fuzzy K-means.The results show that the overall performance of the proposed algorithm is better than other methods.