关键词: 近邻传播算法, 聚类有效性指标, 数据挖掘, 职业能力, 职业倾向

Abstract: Aiming at the problem that the number of clusters in the application of clustering algorithm in educational big data depends on human experience, a new clustering effectiveness index is proposed. The sum of the distances between all samples in the cluster and the cluster center is used to represent the compact density in the cluster, and the minimum value of the sum of the distances between any two clusters is used to represent the degree of separation between clusters. By balancing the relationship between the compact density in the cluster and the degree of separation between clusters, the division of optimal clustering is realized. The test results on UCI and KDD CUP99 data sets show that the clustering quality evaluation results of the new index are effective and reliable. On this basis, a new clustering analysis model is designed by combining with the nearest neighbor propagation algorithm. The model is used to cluster the professional ability of college students. The results show that the new model can accurately give the number of clusters k, effectively excavate students’ career tendency, can provide basis and decision-making for college students’ career potential analysis and enterprises’ talent selection.

Key words: affinity propagation algorithm, cluster validity index, data mining, professional ability, professional tendency