Analysis of Implementation Path of Labor Education Based on Fuzzy Clustering Algorithm

Abstract

Carrying out labor education in schools is an important way to promote people's all-round development, and it is also an important response to who and what kind of people to train. Labor education is an important content of moral education in higher vocational colleges. The content of labor education is designed according to the qualities that workers should possess in the aspects of ideology, psychology, ethics, knowledge, skills and behavior in the new era. On the basis of grasping the specific content of labor education, the specific implementation path of labor education is discussed from the aspects of curriculum labor education, ideological and political labor education, professional labor education, practical labor education and cultural labor education. Among many fuzzy clustering algorithms, FCM algorithm is the most widely used and sensitive one. However, FCM algorithm is sensitive to the initial value, so it is difficult to obtain the global optimum, and it also tends to divide the data set equally. In this paper, the algorithm is improved from two aspects: initialization of cluster center and weighting of objective function. Firstly, the initial clustering center is selected by subtractive clustering method, and then the objective function is weighted by density function to adjust the clustering center. The feasibility of the algorithm is verified by simulation experiments using IRIS data set.