In this paper an efficient class -based data clustering through k -means and KNN approach were applied. It has been applied for proper data grouping and efficient classification. In this approach three dataset have been considered for the experimentation. Data p reprocessing has been performed for the removal of unmatched or empty entry. Then weight assignment and normalization has been performed. K -means and k -nearest neighbor (KNN) have been applied on the dataset for the data grouping and classification purpose . Different splitting variations have been considered with higher variance. Data selection is completely random. The results obtained shows the strength of our approach though classification and class -based clustering.