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A New Picture Safe Semi-supervised Fuzzy Clustering Approach for Noisy Data Partitioning

Năm XB 2026 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 1596 LNNS DOI / Link https://doi.org/10.1007/978-981-95-1746-6_41 ↗

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Tài liệu tham khảo

[1] Huan, P.T., et al.: Enhancing wildfire detection using semi-supervised fuzzy clustering on satellite imagery. In: International Conference on Advances in Information and Communication Technology. Springer Nature Switzerland, Cham (2023)

[2] Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Disc. 2, 169–194 (1998)

[3] Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

[4] Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1, 98–110 (1993)

[5] van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373–440 (2019). https://doi.org/10.1007/s10994-019-05855-6

[6] Lee, J., Kim, Y., Kim, S.B.: Noise-robust graph-based semi-supervised learning with dynamic shaving label propagation. Appl. Soft Comput. 142, 110371 (2023)

[7] Gan, H.: Safe semi-supervised fuzzy c-means clustering. IEEE Access 7, 95659–95664 (2019)

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

[9] Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)

[10] Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press (1981)

[11] Li, Z., et al.: Unified K-means coupled self-representation and neighborhood kernel learning for clustering single-cell RNA-sequencing data. Neurocomputing 501 (2022)

[12] Nayak, J., Swain, B., Nayak, B., Acharya, S.: Fuzzy c-means (FCM) clustering algorithm: a decade review from 2000 to 2014. Comput. Intell. Neurosci. (2015)

[13] Grave, E., Joulin, A., Cissé, M.: Improving neural language models with a continuous cache (2016). arXiv preprint arXiv:1612.04426

[14] Al-Zoubi, M.D., Belal, A.-D.A., Abdelfatah, A.Y.: Fuzzy clustering-based approach for outlier detection. In: Proceedings of the 9th WSEAS international conference on Applications of computer engineering. (2010)

[15] Davidson, I., Ravi, S.S.: Clustering with constraints: feasibility issues and the k-means algorithm. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 138–149 (2005)

[16] Yin, X., Shu, T., Huang, Q.: Semi-supervised fuzzy clustering with metric learning and entropy regularization. Knowl.-Based Syst. 35, 304–311 (2012)

[17] Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, p. 321 (2004)

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

[19] Wang, R., Li, M., Deng, Y.: Multi-granulation picture fuzzy clustering based on neighborhood rough sets. IEEE Access. 8, 108511–108523 (2020)

[20] Thong, P.H., et al.: Picture-neutrosophic trusted safe semi-supervised fuzzy clustering for noisy data. Comput. Syst. Sci. Eng. 46(2) (2023)

[21] Huan, P.T., et al.: TS3FCM: trusted safe semi-supervised fuzzy clustering method for data partition with high confidence. Multimed. Tools Appl. 81(9), 12567–12598 (2022)

[22] Gan, H., et al.: Safe semi-supervised clustering based on Dempster-Shafer evidence theory. Eng. Appl. Artif. Intell. 123, 106334 (2023)