熵加权聚类挖掘算法在学科竞赛学员选拔中的应用

Application of entropy weighted clustering algorithm in selection of subject competition students

针对现有学科竞赛学员选拔中对评估数据缺少有效利用的问题,提出一种基于熵加权聚类的挖掘算法,对学科数据集合进行聚类,从而实现科学合理的人才挑选机制。采用人工统计对数据进行采集和归一化预处理,并利用稀疏分数进行数据特征选择,实现非必要聚类特征的过滤。通过熵加权聚类算法挖掘具有最优解的竞赛成员分配方案。实例分析结果表明,相比标准的Apriori算法,熵加权聚类算法运行效率更高,验证了提出方法的合理性和有效性。

In order to solve the problem of the lack of effective use of the evaluation data in the selection of existing academic contestants,a mining algorithm based on entropy-weighted clustering is proposed to cluster the subject data sets to achieve a scientific and rational mechanism of talent selection. The data is collected and normalized by manual statistic approach,and the sparse scores are used to select the data features for filtering of the non-essential clustering features. The entropy weighted clustering algorithm is used to mine the competition member allocation scheme with the optimal solution. The example analysis results show that the entropy-weighted clustering algorithm is more efficient than the standard Apriori algorithm,which verifies the rationality and effectiveness of the proposed method.