International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.6, No.3, Feb. 2014

Classification via Clustering for Anonymization Data

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Sridhar Mandapati,Raveendra Babu Bhogapathi,M.V.P.C.Sekhara Rao

Index Terms

Privacy Preserving Data Mining (PPDM);Classification;Clustering;K-means;EM;Density based


Due to the exponential growth of hardware technology particularly in the field of electronic data storage media and processing such data, has raised serious issues related in order to protect the individual privacy like ethical, philosophical and legal. Data mining techniques are employed to ensure the privacy. Privacy Preserving Data Mining (PPDM) techniques aim at protecting the sensitive data and mining results. In this study, the different Clustering techniques via classification with and without anonymization data using mining tool WEKA is presented. The aim of this study is to investigate the performance of different clustering methods for the diabetic data set and to compare the efficiency of privacy preserving mining. The accuracy of classification via clustering is evaluated using K-means, Expectation-Maximization (EM) and Density based clustering methods.

Cite This Paper

Sridhar Mandapati, Raveendra Babu Bhogapathi, M.V.P.C.Sekhara Rao,"Classification via Clustering for Anonymization Data", IJCNIS, vol.6, no.3, pp.52-58, 2014. DOI: 10.5815/ijcnis.2014.03.07


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