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 andclustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent ofall children enrolled in schools. The problems of children with specific learning disabilities have been acause of concern to parents and teachers for some time. Decision trees and clustering are powerful andpopular tools used for classification and prediction in Data mining. Different rules extracted from thedecision tree are used for prediction of learning disabilities. Clustering is the assignment of a set ofobservations 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 thedecision tree and K-means algorithm is used for creating the clusters. By applying these classificationtechniques, LD in any child can be identified