An Analysis of Japanese Teaching Behavior Based on the Combination Membership Function

This paper proposes a Japanese teaching behavior analysis algorithm which relies on the combination membership function. First, the teaching model of Japanese teaching is randomly distributed in a planar area as a point on the plane. Then, according to the clustering method of dependency membership function, clustering analysis is carried out from small to large population similarity coefficient. Finally, the recursive algorithm is used to collect the clustering results in the planar area, and the Japanese teaching groups with different teaching characteristics are obtained. The parallel strategy of the algorithm is also proposed, which improves the adaptability of the algorithm to large data volume. This paper takes the Japanese teaching behavior as the experimental data, and compares the result of the algorithm with the results of other classical clustering algorithms. The results show that the Japanese teaching behavior analysis algorithm can meet the requirements of Japanese teaching clustering and classification. Especially in the Japanese teaching analysis and one-on-one teaching analysis of the algorithm has intuitive, obvious characteristics of the characteristics of the category.