Enhancement of Secured Clustering in Health Care Data Sets Using Triple- DES Algorithm to Improve Accuracy in Comparison with Diffie Hellman algorithm

—The objective of this work is to improve the accuracy of secure clustering in health care data sets by using the TripleDES algorithm instead of the Diffie Hellman algorithm. This project is implemented using Triple-DES and it is deployed for secured clustering of healthcare data sets. The sample size taken for Triple-DES is 20 and the Diffie Hellman algorithm is 20 and tested around 10 G-power of 80% and alpha value 0.02. The accuracy of Triple-DES algorithm for secured clustering of healthcare data sets is 73%, which is substantially better than the Diffie Hellman algorithm with a Statistical significance value attained is 0.02 (p < 0.05). Secured Clustering of healthcare data sets using Triple-DES algorithm gives Accuracy (73%) which is significantly better than Diffie Hellman algorithm(60%).