data mining; educational administrative data processing; educational institutions; further education; learning (artificial intelligence); pattern clustering; Mae Fah Luang University; Thai university; data transformation framework; educational data mining; higher education institution; link-based cluster ensemble; student academic performance data; student dropout classification; student enrollment; student enrollment data; student personal data; Accuracy; Clustering algorithms; Data models; Educational institutions; Error analysis; Predictive models; Principal component analysis; classification; cluster ensemble; educational data mining; student dropout;
机译:使用混合类型数据聚类的集合改进泰国大学的学生辍学预测
机译:使用子采样和集成聚类技术来提高不平衡分类的性能
机译:数据融合,集成和聚类可提高韩国道路交通事故严重程度的分类准确性
机译:使用集群集成改善泰国大学学生辍学的分类
机译:最大熵和改进的迭代缩放比例,可用于混合空间的分类,整体分类和扩展。
机译:为期10周的多模式营养教育干预可改善大学生的饮食摄入量:整群随机对照试验
机译:学术风险类型和预测大学生预测辍学的分类模型