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Performance of Euclidean Distance Preserving Perturbation for K-nearest Neighbor Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 105 - Number 2 |
| Year of Publication: 2014 |
| Authors: Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit |
10.5120/18352-9477
|
Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit . Performance of Euclidean Distance Preserving Perturbation for K-nearest Neighbor Classification. International Journal of Computer Applications. 105, 2 ( November 2014), 34-36. DOI=10.5120/18352-9477
Abstract
Data Mining has many applications in the real world. One of the most important and widely found problems is that of classification. Recently, distance preserving data perturbation has gained attention because it mitigates the privacy/accuracy trade-off by guaranteeing perfect accuracy. Many important data mining algorithms can be efficiently applied to the transformed data and produce exactly the same results as if applied to the original data. e. g. ,distance-based clustering and k-nearest neighbor classification. In this research paper we analysis Euclidean distance-preserving data perturbation for k-nearest neighbor classification as a tool for privacy-preserving data mining.
References
- Han Jiawei, M. Kamber, Data Mining: Concepts and Techniques, Beijing: China Machine Press, pp. 1-40,2006.
- M. Artin, Algebra. Prentice Hall, 1991.
- P. H. Schoute, "Le d´eplacement le plus g´en´eral dans l'espace `an dimensions," Annales de l'´Ecole Polytechnique de Delft, vol. 7, pp. 139–158, 1891.
- H. S. M. Coxeter, Regular Polytopes, 2nd ed. , 1963, ch. XII, pp. 213–217.
- G. W. Stewart, "The efficient generation of random orthogonal matrices with an application to condition estimation," SIAM Journal of Numerical Analysis, vol. 17, no. 3, pp. 403–409, 1980.
- P. Diaconis and M. Shahshahani, "The subgroup algorithm for generating uniform random variables," Probability in Engineering and Information Sciences, vol. 1, pp. 15–32, 1987
- K. Chen and L. Liu, "Privacy preserving data classification with rotation perturbation," in Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM'05), Houston, TX, November 2005, pp. 589–592.
- J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, San Diego, CA 92101-4495,USA, 2001.
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