An Analytical Model of College Students’ Mental Health Education Based on the Clustering Algorithm

Abstract

This paper proposes an improved k-means clustering algorithm to analyze the mental health education of college students. It offers an improved k-means clustering algorithm with optimized centroid selection to address the problems of randomly selected class cluster centroids that lead to inconsistent algorithm results and easily fall into local optimal solutions of the traditional k-means clustering algorithm. The algorithm determines the neighborhood parameter based on the Euclidean distance between the data object and its nearest neighbor in the data set. It counts the object density based on the neighborhood parameter Eps. In the initial class cluster centroid selection phase, the algorithm randomly selects the first-class cluster centroid, and subsequent class cluster centroids are chosen based on the data object density information and the distance information between the data object and the existing class cluster centroids. The proposed improved k-means clustering algorithm and clustering validity metrics are tested using several simulated and real datasets. In this paper, the characteristics and application areas of the improved k-means clustering algorithm are sorted out, the self-determination theory related to the enhanced k-means clustering algorithm is investigated, and the behavior of the improved k-means clustering algorithm in the enhanced k-means clustering algorithm system and the octagonal behavior analysis method is also sorted out through the improved k-means clustering algorithm mental health management cases. The path of intervention in mental health education is designed through the improved k-means clustering algorithm. The intervention points are explained, including motivation discovery, mechanism setting, and component matching of the enhanced k-means clustering algorithm.