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Robust Suppressed Competitive Picture Fuzzy Clustering Driven by Entropy

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Abstract

In the fuzzy clustering process, the clustering number for image or text data to be classified is not easy to determine or unknown. The competitive learning algorithm can automatically determine the optimal clustering number to avoid the problem of inappropriate artificial selection. In this paper, based on the clustering by competitive agglomeration (CA), the idea of the “Competitive Learning Mechanism” is introduced to picture fuzzy clustering to obtain the competitive agglomeration picture fuzzy clustering (CAPFCM). The competitive learning regular term of the CAPFCM objective function is reinterpreted from the perspective of minimizing the entropy, and the general framework of the entropy competitive clustering algorithm is constructed. Moreover, the competitive learning regular term of the objective function is replaced by quadratic entropy, Renyi entropy or Shannon entropy to obtain different entropy competitive clustering. To improve the efficiency of the CAPFCM algorithm, the suppressed factor is introduced to appropriately increase the maximum value of the picture fuzzy partition information for different clusters and suppress all others. In addition, this paper proposes a robust adaptive entropy competitive picture fuzzy clustering segmentation algorithm with neighborhood spatial information constraints to enhance the anti-noise ability of the picture fuzzy clustering algorithm for noise image. Experiments show that robust CAPFCM can automatically determine the clustering number and greatly improve the operation efficiency and segmentation performance.

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References

  1. Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Adv. Appl. Pattern Recogn. 22(1171), 203–239 (1981)

    MATH  Google Scholar 

  2. Yu, P., Wang, S.T.: Semi-supervised spatial competitive aggregation algorithm and its application in image segmentation. Comput. Eng. 41(2), 234–241 (2015). (in Chinese)

    Google Scholar 

  3. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geoences 10(2), 191–203 (1984)

    Google Scholar 

  4. Velmurugan, T.: Performance based analysis between K-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data. Appl. Soft Comput. 19, 134–146 (2014)

    Article  Google Scholar 

  5. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    Article  MATH  Google Scholar 

  6. Hung, W.L., Lee, J.S., Fuh, C.D.: Fuzzy clustering based on intuitionistic fuzzy relations. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12(04), 513–529 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cuong, B.C.: Picture fuzzy sets. J. Comput. Sci. Cybern. 30(4), 409–420 (2014)

    Google Scholar 

  8. Thong, P.H., Son, L.H.: Picture fuzzy clustering: a new computational intelligence method. Soft. Comput. 20(9), 3549–3562 (2016)

    Article  MATH  Google Scholar 

  9. Wu, C.M., Wu, Q.P.: A robust image segmentation algorithm based on improved PFCM. J. Xi’an Univ. Posts Telecommun. 22(05), 37–43 (2017). (in Chinese)

    Google Scholar 

  10. Yu, P., Wang, S.: Application of semi-supervised CA algorithm based on point density in image clustering. J. Nanjing Univ. 50(04), 447–456 (2014). (in Chinese)

    Google Scholar 

  11. Frigui, H., Krishnapuram, R.: Clustering by competitive agglomeration. Pattern Recogn. 30(7), 1109–1119 (1997)

    Article  Google Scholar 

  12. Huang, C., Chung, F.L., Wang, S.: Generalized competitive agglomeration clustering algorithm. Int. J. Mach. Learn. Cybern. 8(6), 1945–1969 (2016)

    Article  Google Scholar 

  13. Zhang, D., Chen, S., Zhou, Z.H.: Entropy-inspired competitive clustering algorithms. J. Softw. 1, 67–84 (2007)

    Google Scholar 

  14. Fan, J.L., Zhen, W.Z., Xie, W.X.: Suppressed fuzzy C-means clustering algorithm. Pattern Recogn. Lett. 24(9), 1607–1612 (2003)

    Article  MATH  Google Scholar 

  15. Fan, J.L., Li, J.: A fixed suppressed rate selection method for suppressed fuzzy C-means clustering algorithm. Appl. Math. 05(8), 1275–1283 (2014)

    Article  Google Scholar 

  16. Li J, Fan J. Parameter selection for suppressed fuzzy C-means clustering algorithm based on fuzzy partition entropy. In: International Conference on Fuzzy Systems & Knowledge Discovery. IEEE (2014)

  17. Zhang, H., Wang, Q., Shi, W., et al.: A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification. IEEE Trans. Geosci. Remote Sens. 55(9), 5057–5068 (2017)

    Article  Google Scholar 

  18. Yanfei, Z., Ailong, M., Liangpei, Z.: An adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 7(4), 1235–1248 (2014)

    Article  Google Scholar 

  19. Zhao, F., Fan, J., Liu, H.: Optimal-selection-based suppressed fuzzy C-means clustering algorithm with self-tuning non local spatial information for image segmentation. Expert Syst. Appl. 41(9), 4083–4093 (2014)

    Article  Google Scholar 

  20. Szilágyi, L., Szilágyi, S.M., Benyó, Z.: Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models. Soft. Comput. 14(5), 495–505 (2009)

    Article  MATH  Google Scholar 

  21. Guo, F.F., Shen, J., Wang, X.X.: Adaptive fuzzy C-means algorithm based on local noise detecting for image segmentation. IET Image Proc. 10(4), 272–279 (2016)

    Article  Google Scholar 

  22. Guo, Y.H., Sengur, A.: A novel color image segmentation approach based on neutrosophic set and modified fuzzy C-means. Circuits Syst. Signal Process. 32(4), 1699–1723 (2012)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (51709228) and the Natural Science Foundation of Shaanxi Province (2017JM6107).

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Authors and Affiliations

  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Chengmao Wu & Na Liu

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  1. Chengmao Wu
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  2. Na Liu
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Correspondence to Na Liu.

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Wu, C., Liu, N. Robust Suppressed Competitive Picture Fuzzy Clustering Driven by Entropy. Int. J. Fuzzy Syst. 22, 2466–2492 (2020). https://doi.org/10.1007/s40815-020-00937-3

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  • DOI: https://doi.org/10.1007/s40815-020-00937-3

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