Significance of Classification Techniques in Prediction of Learning
  Disabilities
  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. Clustering
is the assignment of a set of observations into subsets, called clusters, which
are useful in finding the different signs and symptoms (attributes) present in
the LD affected child. In this paper, J48 algorithm is used for constructing
the decision tree and K-means algorithm is used for creating the clusters. By
applying these classification techniques, LD in any child can be identified.
