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Reseach Article

Performance of Euclidean Distance Preserving Perturbation for K-nearest Neighbor Classification

by Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit
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

@article{ 10.5120/18352-9477,
author = { Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit },
title = { Performance of Euclidean Distance Preserving Perturbation for K-nearest Neighbor Classification },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 2 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number2/18352-9477/ },
doi = { 10.5120/18352-9477 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:40.142338+05:30
%A Bhupendra Kumar Pandya
%A Umesh Kumar Singh
%A Keerti Dixit
%T Performance of Euclidean Distance Preserving Perturbation for K-nearest Neighbor Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 2
%P 34-36
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
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
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Index Terms

Computer Science
Information Sciences

Keywords

K- nearest neighbor classification