期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：7
出版社：S&S Publications
摘要：This paper highlights the study of two classification methods, Rough Sets Theory (RST) and DecisionTrees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications ofdata mining. Learning disability prediction is a very complicated task. By using these two classification methods wecan easily and accurately predict LD in any child. Also, we can determine the best classification method. In this study,rule mining is performed using the algorithms LEM1 in rough sets and J48 in construction of decision trees. From thisstudy, it is concluded that, the performance of decision trees may be considerably poorer in several important aspectscompared to that of rough sets theory. It is found that, for selection of attributes, RST is very useful especially in thecase of inconsistent data.
关键词：Decision Tree; Learning Disability; Rough Sets; Rule Mining; Support and Confidence
