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Abstract:
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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 |