1. Aggarwal, C.C, Yu, P.S.: Finding generalized projected clusters in high dimensional spaces. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 70–81 (2000)

2. Aggarwal, C.C., Wolf, J.L., Yu, P.S., et al.: Fast algorithms for projected clustering. ACM SIGMoD Record28(2), 61–72 (1999)

   Article  Google Scholar 

3. Agrawal, R., Gehrke, J., Gunopulos, D., et al.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 94–105 (1998)

4. Araujo, A.F., Antonino, V.O., Ponce-Guevara, K.L.: Self-organizing subspace clustering for high-dimensional and multi-view data. Neural Netw.130, 253–268 (2020)

   Article  Google Scholar 

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

   MATH  Google Scholar 

6. Bian, Z., Ishibuchi, H., Wang, S.: Joint learning of spectral clustering structure and fuzzy similarity matrix of data. IEEE Trans. Fuzzy Syst.27(1), 31–44 (2019)

   Article  Google Scholar 

7. Bo, D., Wang, X., Shi, C., et al.: Structural deep clustering network. Proc. Web Conf.2020, 1400–1410 (2020)

   Google Scholar 

8. Boyd, S., Parikh, N., Chu, E., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn.3(1), 1–122 (2011)

   Article  Google Scholar 

9. Chan, E.Y., Ching, W.K., Ng, M.K., et al.: An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognit.37(5), 943–952 (2004)

   Article  Google Scholar 

10. Cheng, C.H., Fu, A.W., Zhang, Y.: Entropy-based subspace clustering for mining numerical data. In: Proceedings of the ACM SIGKDD international conference on Knowledge discovery and data mining, pp 84–93 (1999)

11. Deng, Z., Choi, K.S., Jiang, Y., et al.: A survey on soft subspace clustering. Inf. Sci.348, 84–106 (2016)

    Article MathSciNet  Google Scholar 

12. Dua, D., Graff, C.: UCI machine learning repository.http://archive.ics.uci.edu/ml (2017)

13. Gan, G., Wu, J.: A convergence theorem for the fuzzy subspace clustering (fsc) algorithm. Pattern Recognit.41(6), 1939–1947 (2008)

    Article  Google Scholar 

14. Gao, Y., Wang, Z., Li, H., et al.: Gaussian collaborative fuzzy c-means clustering. Int. J. Fuzzy Syst.21, 1–17 (2021)

    Google Scholar 

15. Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst.161(4), 522–543 (2010)

    Article MathSciNet  Google Scholar 

16. Guo, L., Chen, L., Lu, X., et al.: Membership affinity lasso for fuzzy clustering. IEEE Trans. Fuzzy Syst.28(2), 294–307 (2020)

    Article  Google Scholar 

17. Hallac, D., Leskovec, J., Boyd, S.: Network lasso: Clustering and optimization in large graphs. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 387–396 (2015)

18. He, X., Cai, D., Shao, Y., et al.: Laplacian regularized gaussian mixture model for data clustering. IEEE Trans. Knowl. Data Eng.23(9), 1406–1418 (2011)

    Article  Google Scholar 

19. Huang, J., Ng, M., Rong, H., et al.: Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell.27(5), 657–668 (2005)

    Article  Google Scholar 

20. Jin, L., Zhao, S., Zhang, C., et al.: Adaptive soft subspace clustering combining within-cluster and between-cluster information. J. Intell. Fuzzy Syst.38(3), 3319–3330 (2020)

    Article  Google Scholar 

21. Jing, L., Ng, M.K., Huang, J.Z.: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans. Knowl. Data Eng.19(8), 1026–1041 (2007)

    Article  Google Scholar 

22. Lq, Li., Xl, Wang, Zx, Liu, et al.: A novel intuitionistic fuzzy clustering algorithm based on feature selection for multiple object tracking. Int. J. Fuzzy Syst.21(5), 1613–1628 (2019)

    Article  Google Scholar 

23. Li, X., Cui, G., Dong, Y.: Graph regularized non-negative low-rank matrix factorization for image clustering. IEEE Trans. Cybernet.47(11), 3840–3853 (2017)

    Article MathSciNet  Google Scholar 

24. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, pp 281–297 (1967)

25. Mirzal, A.: Statistical analysis of microarray data clustering using nmf, spectral clustering, kmeans, and gmm. IEEE/ACM Transactions on Computational Biology and Bioinformatics pp 1–1.https://doi.org/10.1109/TCBB.2020.3025486 (2020)

26. Modha, D.S., Spangler, W.S.: Feature weighting in k-means clustering. Mach. Learning52(3), 217–237 (2003)

    Article  Google Scholar 

27. Nagesh, H., Goil, S., Choudhary, A.: Mafia: Efficient and scalable subspace clustering for very large data sets. Technical Report 9906–010 (1999)

28. Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp. 977–986 (2014)

29. Procopiuc, C.M., Jones, M., Agarwal, P.K., et al.: A monte carlo algorithm for fast projective clustering. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 418–427 (2002)

30. Shen, H., Yang, J., Wang, S., et al.: Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft Comput.10(11), 1061–1073 (2006)

    Article  Google Scholar 

31. Wang, J., Deng, Z., Choi, K.S., et al.: Distance metric learning for soft subspace clustering in composite kernel space. Pattern Recognit.52, 113–134 (2016)

    Article  Google Scholar 

32. Wang, X., Wang, Y., Wang, L.: Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognit. Lett.25(10), 1123–1132 (2004)

    Article  Google Scholar 

33. Woo, K.G., Lee, J.H., Kim, M.H., et al.: Findit: a fast and intelligent subspace clustering algorithm using dimension voting. Inf. Softw. Technol.46(4), 255–271 (2004)

    Article  Google Scholar 

34. Wu, C., Liu, N.: Robust suppressed competitive picture fuzzy clustering driven by entropy. Int. J. Fuzzy Syst.22(8), 2466–2492 (2020)

    Article  Google Scholar 

35. Yan, S., Xu, D., Zhang, B., et al.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell.29(1), 40–51 (2007)

    Article  Google Scholar 

36. Yang, J., Wang, W., Wang, H., et al.: /spl delta/-clusters: capturing subspace correlation in a large data set. In: Proceedings the 18th international conference on data engineering, pp. 517–528 (2002)

37. Yang, M.S., Nataliani, Y.: Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognit.71, 45–59 (2017).https://doi.org/10.1016/j.patcog.2017.05.017

    Article  Google Scholar 

38. Yang, M.S., Nataliani, Y.: A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy. IEEE Trans. Fuzzy Syst.26(2), 817–835 (2018)

    Article  Google Scholar 

39. Ye, X., Zhao, J., Chen, Y., et al.: Bayesian adversarial spectral clustering with unknown cluster number. IEEE Trans. Image Process.29, 8506–8518 (2020).https://doi.org/10.1109/TIP.2020.3016491

    Article MathSciNet  Google Scholar 

40. Zhao, Y.P., Chen, L., Chen, C.P.: Fuzzy clustering in cascaded feature space. Int. J. Fuzzy Syst.21(7), 2155–2167 (2019)

    Article  Google Scholar 

41. Zhou, J., Chen, C.L.P., Chen, L., et al.: A collaborative fuzzy clustering algorithm in distributed network environments. IEEE Trans. Fuzzy Syst.22(6), 1443–1456 (2014)

    Article  Google Scholar 

42. Zhou, J., Chen, L., Chen, C.P., et al.: Fuzzy clustering with the entropy of attribute weights. Neurocomputing198, 125–134 (2016)

    Article  Google Scholar 

Download references