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Preservation of Datasets from Unauthorized Party and Classify it using Modified C5.0 Algorithm

Author(s):

Kanani Arjun , Parul institute of Engginering and Technology,Vadodara,Gujarat

Keywords:

Decision tree algorithm, ID3 and C4.5 classifier, C5.0 classifier unrealized training datasets.

Abstract

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 C5.0 and C5.0 is the extension version of C4.5 algorithm. C%.0 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 C5.0 algorithm which uses datasets generated by unrealized training dataset for classification. As the memory consumption and time consumption rate of C5.0 is better compared to C4.5 which is useful during large dataset entries to securely transfer and regenerate original data from modified C5.0 classification method.

Other Details

Paper ID: IJSRDV2I3428
Published in: Volume : 2, Issue : 3
Publication Date: 01/06/2014
Page(s): 752-755

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