SVD-based factorization technique for dual privacy protection data mining

Jie Tang; Jun Zhang; Xinyu Geng; Bo Peng

doi:10.1109/ICCIS.2011.269

SVD-based factorization technique for dual privacy protection data mining

Jie Tang, Jun Zhang, Xinyu Geng, Bo Peng

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Singular value decomposition (SVD) method is a very important matrix decomposition method in linear algebra. It is widely used in signal processing, statistics, data compression and other fields. The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique, and then presents new methods for dual private protection data mining. Finally we conduct experiments to test and verify the proposed approach and get good results.

Original language	English
Title of host publication	Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011
Pages	357-360
Number of pages	4
DOIs	https://doi.org/10.1109/ICCIS.2011.269
State	Published - 2011
Event	2011 International Conference on Computational and Information Sciences, ICCIS 2011 - Chengdu, Sichuan, China Duration: Oct 21 2011 → Oct 23 2011

Publication series

Name	Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011

Conference

Conference	2011 International Conference on Computational and Information Sciences, ICCIS 2011
Country/Territory	China
City	Chengdu, Sichuan
Period	10/21/11 → 10/23/11

Keywords

K-means
Latent Semantic Analysis
PDDPM
Singular value decomposition

ASJC Scopus subject areas

Computational Theory and Mathematics
Information Systems

Access to Document

10.1109/ICCIS.2011.269

Cite this

Tang, J., Zhang, J., Geng, X., & Peng, B. (2011). SVD-based factorization technique for dual privacy protection data mining. In Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011 (pp. 357-360). Article 6086208 (Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011). https://doi.org/10.1109/ICCIS.2011.269

@inproceedings{256d675ea97b46fe85a22a4c975b1d51,

title = "SVD-based factorization technique for dual privacy protection data mining",

abstract = "Singular value decomposition (SVD) method is a very important matrix decomposition method in linear algebra. It is widely used in signal processing, statistics, data compression and other fields. The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique, and then presents new methods for dual private protection data mining. Finally we conduct experiments to test and verify the proposed approach and get good results.",

keywords = "K-means, Latent Semantic Analysis, PDDPM, Singular value decomposition",

author = "Jie Tang and Jun Zhang and Xinyu Geng and Bo Peng",

year = "2011",

doi = "10.1109/ICCIS.2011.269",

language = "English",

isbn = "9780769545011",

series = "Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011",

pages = "357--360",

booktitle = "Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011",

note = "2011 International Conference on Computational and Information Sciences, ICCIS 2011 ; Conference date: 21-10-2011 Through 23-10-2011",

}

Tang, J, Zhang, J, Geng, X & Peng, B 2011, SVD-based factorization technique for dual privacy protection data mining. in Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011., 6086208, Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011, pp. 357-360, 2011 International Conference on Computational and Information Sciences, ICCIS 2011, Chengdu, Sichuan, China, 10/21/11. https://doi.org/10.1109/ICCIS.2011.269

SVD-based factorization technique for dual privacy protection data mining. / Tang, Jie; Zhang, Jun; Geng, Xinyu et al.
Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011. 2011. p. 357-360 6086208 (Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SVD-based factorization technique for dual privacy protection data mining

AU - Tang, Jie

AU - Zhang, Jun

AU - Geng, Xinyu

AU - Peng, Bo

PY - 2011

Y1 - 2011

N2 - Singular value decomposition (SVD) method is a very important matrix decomposition method in linear algebra. It is widely used in signal processing, statistics, data compression and other fields. The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique, and then presents new methods for dual private protection data mining. Finally we conduct experiments to test and verify the proposed approach and get good results.

AB - Singular value decomposition (SVD) method is a very important matrix decomposition method in linear algebra. It is widely used in signal processing, statistics, data compression and other fields. The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique, and then presents new methods for dual private protection data mining. Finally we conduct experiments to test and verify the proposed approach and get good results.

KW - K-means

KW - Latent Semantic Analysis

KW - PDDPM

KW - Singular value decomposition

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DO - 10.1109/ICCIS.2011.269

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T3 - Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011

SP - 357

EP - 360

BT - Proceedings - 2011 International Conference on Computational and Information Sciences, ICCIS 2011

T2 - 2011 International Conference on Computational and Information Sciences, ICCIS 2011

Y2 - 21 October 2011 through 23 October 2011

ER -

SVD-based factorization technique for dual privacy protection data mining

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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