Rotation Perturbation Technique for Privacy Preserving in Data Stream Mining

Authors

  • Kalyani Kathwadia  Department of Information Technology Silver Oak College of Engineering And Technology Ahmedabad, Gujarat, India
  • Aniket Patel  Department of Information Technology Silver Oak College of Engineering And Technology Ahmedabad, Gujarat, India

Keywords:

Data mining, Classification, Privacy, PCA, Z score-Normalization

Abstract

Datasets is very challenging task in the systems. It is real processing. Data mining technique classification is one of the most important technique, in this paper is to classify the data as improve the classification accuracy, we have used ensemble model for classification of data. Randomization process added to privacy sensitive data after next process reconstruction to the main data from the perturbed data. Principal Component Analysis (PCA) is used to preserve the variability in the data. Rotation transformation can enlarge the increase the base classifiers and improve the accuracy of the ensemble classifier. In this paper, we analyses a rotation perturbation technique for PCA find eigenvector, load line plot and Zscore-Normalization method using to dimension in stream mining.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-06-30

Issue

Volume 4, Issue 8, May-June-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kalyani Kathwadia, Aniket Patel, " Rotation Perturbation Technique for Privacy Preserving in Data Stream Mining, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.217-223, May-June-2018.