apply the same approach for large data set consisting of all relevant attributes. This study is a true comparison of the proposed approach by applying it to large datasets and analyzing the completeness and effectiveness of the generated rules. J48 decision tree application on discrete data and twofold test shows that it is better than RST in terms of efficiency and complexity. J48 decision tree has to be applied on continuous or categorical data. Noise effects and their elimination have to be studied. The results from the experiments on these small datasets suggests that J48 decision tree can serve as a model for classification as it generates simpler rules and remove irrelevant attributes at a stage prior to tree induction. By using clustering method, the number of clusters can easily be identified in case of very large data sets. In this paper, we are considering an approach to handle learning disability database and predicting the learning disability in school age children. Our future research work focuses on, fuzzy sets, to predict the percentage of LD, in each child, thus to explore the possibilities of getting more accurate and effective results in prediction of LD. 

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