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Semi-supervised Fuzzy c-Means Variants: A Study on Noisy Label Supervision

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Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations (IPMU 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 854))

Abstract

Semi-supervised clustering algorithms aim at discovering the hidden structure of data sets with the help of expert knowledge, generally expressed as constraints on the data such as class labels or pairwise relations. Most of the time, the expert is considered as an oracle that only provides correct constraints. This paper focuses on the case where some label constraints are erroneous and proposes to investigate into more detail three semi-supervised fuzzy c-means clustering approaches as they have been tailored to naturally handle uncertainty in the expert labeling. In order to run a fair comparison between existing algorithms, formal improvements have been proposed to guarantee and fasten their convergence. Experiments conducted on real and synthetical datasets under uncertain labels and noise in the constraints show the effectiveness of using fuzzy clustering algorithm for noisy semi-supervised clustering.

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Notes

  1. 1.

    Available at http://archive.ics.uci.edu/ml.

References

  1. Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC, Boca Raton (2008)

    MATH  Google Scholar 

  2. Bouchachia, A., Pedrycz, W.: Enhancement of fuzzy clustering by mechanisms of partial supervision. Fuzzy Sets Syst. 157(13), 1733–1759 (2006)

    Article  MathSciNet  Google Scholar 

  3. Antoine, V., Quost, B., Masson, M.H., Denœux, T.: Evidential clustering with instance-level constraints for proximity data. Soft. Comput. 18(7), 1321–1335 (2014)

    Article  Google Scholar 

  4. Basu, S., Banerjee, A., Mooney, R.: Active semi-supervision for pairwise constrained clustering. In: Proceedings of 2004 SIAM Interernational Conference on Data Mining, pp. 333–344 (2004)

    Chapter  Google Scholar 

  5. Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of 21st ICML (2004)

    Google Scholar 

  6. Wagstaff, K.L.: When is constrained clustering beneficial, and why. In: AAAI (2006)

    Google Scholar 

  7. Vu, V., Labroche, N., Bouchon-Meunier, B.: Boosting clustering by active constraint selection. In: Proceedings of 2010 19th ECAI, pp. 297–302 (2010)

    Google Scholar 

  8. Vu, V., Labroche, N., Bouchon-Meunier, B.: An efficient active constraint selection algorithm for clustering. In: 20th ICPR, pp. 2969–2972 (2010)

    Google Scholar 

  9. Zhang, D., Tan, K., Chen, S.: Semi-supervised kernel-based fuzzy c-means. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 1229–1234. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30499-9_191

    Chapter  Google Scholar 

  10. Pedrycz, W., Waletzky, J.: Fuzzy clustering with partial supervision. IEEE Trans. Syst. Man Cybern. Part B Cybern. 27(5), 787–795 (1997)

    Article  Google Scholar 

  11. Lai, D., Garibaldi, J.: A comparison of distance-based semi-supervised fuzzy c-means clustering algorithms. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1580–1586 (2011)

    Google Scholar 

  12. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Advanced Applications in Pattern Recognition. Springer, New York (1981). https://doi.org/10.1007/978-1-4757-0450-1

    Book  MATH  Google Scholar 

  13. Gustafson, D., Kessel, W.: Fuzzy clustering with a fuzzy covariance matrix. In: IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, pp. 761–766 (1979)

    Google Scholar 

  14. Endo, Y., Hamasuna, Y., Yamashiro, M., Miyamoto, S.: On semi-supervised fuzzy c-means clustering. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1119–1124 (2009)

    Google Scholar 

  15. Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proceedings of 19th International Conference on Machine Learning (ICML), pp. 27–34 (2002)

    Google Scholar 

  16. Basu, S., Bilenko, M., Banerjee, A., Mooney, R.: Probabilistic Semi-supervised Clustering with Constraints, pp. 71–98. MIT Press, Cambridge (2006)

    Google Scholar 

  17. Rand, W.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  18. Dave, R.: Validating fuzzy partitions obtained through c-shells clustering. Pattern Recogn. Lett. 17(6), 613–623 (1996)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Clermont Auvergne University, UMR 6158, LIMOS, 63006, Clermont-Ferrand, France

    Violaine Antoine

  2. University of Tours, EA 6300, LIFAT, Tours, France

    Nicolas Labroche

Authors
  1. Violaine Antoine
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  2. Nicolas Labroche
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Corresponding author

Correspondence to Violaine Antoine .

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cádiz, Cadiz, Spain

    Jesús Medina

  2. Universidad de Málaga, Málaga, Málaga, Spain

    Manuel Ojeda-Aciego

  3. Universidad de Granada, Granada, Spain

    José Luis Verdegay

  4. Universidad de Granada, Granada, Spain

    David A. Pelta

  5. Universidad de Málaga, Málaga, Málaga, Spain

    Inma P. Cabrera

  6. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, New York, USA

    Ronald R. Yager

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Antoine, V., Labroche, N. (2018). Semi-supervised Fuzzy c-Means Variants: A Study on Noisy Label Supervision. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-91476-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91475-6

  • Online ISBN: 978-3-319-91476-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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