Analysis of Influencing Factors on Excellent Teachers' Professional Growth based on DB-Kmeans Method

Abstract The Kmeans clustering algorithm is widely used for the advantages of simplicity and efficient operation. However, the lack of clustering centers in the algorithm usually causes incorrect category of some discrete points. Therefore, in order to obtain more accurate clustering results when studying the factors affecting the professional growth of outstanding teachers, this paper proposes an improved algorithm of Kmeans combined with DBSCAN. Observing the clustering results of the influencing factors and calculating the evaluation standard values of the clustering results, it is found that the optimized DB-Kmeans algorithm has obvious improvements in the accuracy of the clustering results and the clustering speed, and the clustering effect of the algorithm on edge points is more advantageous than the original algorithms according to the scatter diagram.