Preservation of the Sample Data with Help of Unrealized Training Datasets and later classifying it Using Modified C4.5 Algorithm

In order to protect the data centrally when they are being transferred from one party to another party so, that it cannot be used for secondary purposes unrealized training dataset is an important technique used to prevent data. With help of Unrealized training dataset algorithm it divides the sample data in two forms i.e. Tp a set of perturbing datasets and T’ a set of output training datasets. The classification method used over here is C4.5 and C4.5 is the extension version of ID3 algorithm. C4.5 is the classification decision tree algorithm which uses features like handling both continuous and discrete attributes, handling missing values, purning techniques. This paper produces a modified C4.5 algorithm which uses datasets generated by unrealized training dataset for classification. As the memory consumption and time consumption rate of C4.5 is better compared to ID3 which is useful during large dataset entries to securely transfer and regenerate original data from modified C4.5 classification method.