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Collaborative Framework for Fuzzy Co-clustering

  • Tin-Chih Toly ChenEmail author
  • Katsuhiro Honda
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Privacy preserving data mining is a fundamental approach for utilizing multiple databases including personal or sensitive information without fear of information leaks. In this chapter, a framework of securely applying fuzzy co-clustering to multiple cooccurrence information, which is stored in multiple organizations, is reviewed with illustrative examples.

References

  1. 1.
    C.C. Aggarwal, P.S. Yu, Privacy-Preserving Data Mining: Models and Algorithms (Springer-Verlag, New York, 2008)Google Scholar
  2. 2.
    P. Samarati, Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  3. 3.
    L. Sweeney, \(k\)-anonymity: a model for protecting privacy. Int. J. Uncertain., Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  4. 4.
    J.B. MacQueen, Some methods of classification and analysis of multivariate observations, inProceeding of 5th Berkeley Symposium on Mathematical Statistics and Probability (1967), pp. 281–297Google Scholar
  5. 5.
    T.-K. Yu, D.T. Lee, S.-M. Chang, J. Zhan, Multi-party \(k\)-means clustering with privacy consideration, Proceeding of the International Symposium on Parallel and Distributed Processing with Applications (2010), pp. 200–207Google Scholar
  6. 6.
    F. Meskine, S.N. Bahloul, Privacy preserving \(k\)-means clustering: a survey research. Int. Arab. J. Inf. Technol. 9(2), 194–200 (2012)Google Scholar
  7. 7.
    K. Honda, T. Oda, D. Tanaka, A. Notsu, A collaborative framework for privacy preserving fuzzy co-clustering of vertically distributed cooccurrence matrices, Adv. Fuzzy Syst., 2015, #729072, 1–8 (2015)Google Scholar
  8. 8.
    J. Vaidya, C. Clifton, Privacy-preserving \(K\)-means clustering over vertically partitioned data, Proceeding of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Washington, DC, USA 2003), pp. 206–215Google Scholar
  9. 9.
    S. Samet, A. Miri, L. Orozco-Barbosa, Privacy preserving \(k\)-means clustering in multi-party environment, in Proceeding of the International Conference on Security and Cryptography (2007), pp. 381–385Google Scholar
  10. 10.
    S. Jha, L. Kruger, P. Mcdaniel, Privacy preserving clustering, in Proceeding of the 10th European Symposium On Research In Computer Security (2005) pp. 397–417CrossRefGoogle Scholar
  11. 11.
    C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, M.Y. Zhu, Tools for privacy preserving distributed data mining. ACM SIGKDD Explor. Newsl. 4(2), 28–34 (2002)CrossRefGoogle Scholar
  12. 12.
    J. Vaidya, A survey of privacy-preserving methods across vertically partitioned data, Privacy-preserving Data Mining: Models and Algorithms (Springer, 2008), pp. 337–358Google Scholar
  13. 13.
    A. İnan, S.V. Kaya, Y. Saygın, E. Savaş, A.A. Hintoğlu, A. Levi, Privacy preserving clustering on horizontally partitioned data. Data & Knowl. Eng. 63, 646–666 (2007)CrossRefGoogle Scholar
  14. 14.
    J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, 1981)Google Scholar
  15. 15.
    T.C. Havens, J.C. Bezdek, C. Leckie, L.O. Hall, M. Palaniswami, Fuzzy \(c\)-means algorithms for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (2012)CrossRefGoogle Scholar
  16. 16.
    N. Pal, J.C. Bezdek, Complexity reduction for large image processing, IEEE Trans. Syst., Man, Cybern., 32(5), 598–611 (2002)Google Scholar
  17. 17.
    P. Hore, L. Hall, D. Goldgof, Single pass fuzzy \(c\)-means, in Proceeding of IEEE International Conference on Fuzzy Systems (2007), pp. 1–7Google Scholar
  18. 18.
    P. Hore, L. Hall, D. Goldgof, Y. Gu, A. Maudsley, A scalable framework for segmenting magnetic resonance images. J. Signal Process. Syst. 54(1–3), 183–203 (2009)CrossRefGoogle Scholar
  19. 19.
    C.-H. Oh, K. Honda, H. Ichihashi, Fuzzy clustering for categorical multivariate data, in Proceeding of Joint 9th IFSA World Congress and 20th NAFIPS International Conference (2001), pp. 2154–2159Google Scholar
  20. 20.
    S. Miyamoto, M. Mukaidono, Fuzzy \(c\)-means as a regularization and maximum entropy approach, in Proceeding of the 7th International Fuzzy Systems Association World Congress, vol. 2 (1997), pp. 86–92Google Scholar
  21. 21.
    S. Miyamoto, H. Ichihashi, K. Honda, Algorithms for Fuzzy Clustering (Springer, 2008)Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Industrial Engineering and ManagementNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Graduate School of EngineeringOsaka Prefecture UniversitySakaiJapan

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