基于近邻传播聚类的职业能力评价模型

针对聚类算法在教育大数据应用中存在的聚类数目依赖人工经验等问题，提出一种新的聚类有效性指标，用簇内全部样本与簇中心的距离之和表示簇内紧密度，用任意两簇间样本距离和的最小值表示簇间分离度，通过平衡簇内紧密度和簇间分离度之间的关系，实现最优聚类的划分。在UCI和KDD CUP99数据集上的测试结果表明，新指标的聚类质量评价结果有效、可靠。在此基础上，结合近邻传播算法设计新的聚类分析模型，使用该模型对某高校学生的职业能力进行聚类分析，结果表明：新模型能够准确地给出聚类数目k，有效地挖掘出学生的职业倾向，可以为大学生职业潜能分析、企业的人才选择提供依据与决策。

Occupational Competence Evaluation Model Based on Affinity Propagation Clustering

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.