The aim of this study is to show the importance of two classification
techniques, viz. decision tree and clustering, in prediction of learning
disabilities (LD) of school-age children. LDs affect about 10 percent of all
children enrolled in schools. The problems of children with specific learning
disabilities have been a cause of concern to parents and teachers for some
time. Decision trees and clustering are powerful and popular tools used for
classification and prediction in Data mining. Different rules extracted from
the decision tree are used for prediction of learning disabilities.