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, lack centers in usually causes incorrect category some discrete points. Therefore, order to obtain more accurate results when studying factors affecting professional growth outstanding teachers, this paper proposes an improved combined with DBSCAN. Observing influencing calculating evaluation standard values results, it found that optimized DB-Kmeans has obvious improvements accuracy effect on edge points advantageous than original algorithms according scatter diagram.