半监督学习在研究生调剂中的应用

研究生调剂是研究生招生中的重要环节。传统的调剂方法都是通过手工操作的,考生很难从往年大量的调剂数据中分析出规律,选报合适的学校。提出了基于半监督学习的数据挖掘方法,也即是从已知类别的训练样本提取出其中的关联规则作为分类的监督信息,并结合非监督学习方法中的K-mean聚类算法,对大量未标识样本进行分类的算法,此方法克服了研究生调剂涉及因素繁多,无法准确填报的弊端。该方法实现过程简单,分类准确,可推广性较强。

Application of Semi-Supervised Learning to Graduate Adjusting

Graduate Adjusting is an important step for Graduate Admission.The traditional adjusting methods which are all manual,make it very hard for students to choose a proper school from a huge number of data.This paper proposes a data-mining method based on semi-supervised study.Using the association rules,which are extracted from the labeled training samples,as supervised information,and combining with the K-mean algorithm in non-supervised study method,this paper elaborates on the semi-supervised study algorith...