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Two approach comparisons for relative evaluation of faculty performance using data mining techniques

机译:使用数据挖掘技术对教师绩效进行相对评估的两种方法比较

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摘要

Educational data mining (EDM) is one of the applications of data mining. In educational data mining, there are two key domains, i.e. student domain and faculty domain. Different type of research work has been done in both domains. In this paper we define two approaches one is multiple classifier approach and the other is a single classifier approach and comparing them, for relative evaluation of faculty performance using data mining techniques. In multiple classifier approach K-nearest neighbor (KNN) is used in first step and Rule based classification is used in the second step of classification while in single classifier approach only KNN is used in both steps of classification. The results of both approaches are compared to adopting the best approach in the organization for decision making.
机译:教育数据挖掘(EDM)是数据挖掘的应用之一。在教育数据挖掘中,有两个关键领域,即学生领域和教师领域。在这两个领域已经完成了不同类型的研究工作。在本文中,我们定义了两种方法,一种是多分类器方法,另一种是单分类器方法并进行比较,以使用数据挖掘技术对教师的绩效进行相对评估。在多分类器方法中,第一步使用K近邻(KNN),在分类的第二步中使用基于规则的分类,而在单分类器方法中,两个分类步骤中仅使用KNN。将两种方法的结果与在组织中采用最佳方法进行决策进行比较。

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