[1] J MACQUEEN. Some methods for classification and analysis of multivariate observations. Proc. Symp. Math. Statist. and Probability, 1(1967).

[2] J C DUNN. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32-57(1973).

[3] J C BEZDEK, R EHRLICH, W FULL. FCM： the fuzzy c-means clustering algorithm. Computers & Geosciences, 10, 191-203(1984).

[4] N R PAL, J C BEZDEK. On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3, 370-379(1995).

[5] A Y NG, M I JORDAN, Y WEISS. On Spectral Clustering： Analysis and an algorithm. proc nips(2002).

[6] J YU, M S YANG. Optimality test for generalized FCM and its application to parameter selection. IEEE Transactions on Fuzzy Systems, 13, 164-176(2005).

[7] J YU, M S YANG. A generalized fuzzy clustering regularization model with optimality tests and model complexity analysis. IEEE Transactions on Fuzzy Systems, 15, 904-915(2007).

[8] J L XU, J W HAN, K XIONG et al. Robust and sparse fuzzy K-means clustering, 2224-2230(2016).

[9] X Y CHANG, Q N WANG, Y W LIU et al. Sparse regularization in fuzzy c-means for high-dimensional data clustering. IEEE Transactions on Cybernetics, 47, 2616-2627(2017).

[10] R ZHANG, F P NIE, M H GUO et al. Joint learning of fuzzy k-means and nonnegative spectral clustering with side information. IEEE Transactions on Image Processing, 28, 2152-2162(2019).

[11] R Zhang, H Tong, Y Xia et al. Robust Embedded Deep K-means Clustering(2019).

[12] K S CHUANG, H L TZENG, S CHEN et al. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, 30, 9-15(2006).

[13] W L CAI, S C CHEN, D Q ZHANG. Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 40, 825-838(2007).

[14] F P NIE, X Q WANG, H HUANG. Clustering and projected clustering with adaptive neighbors(2014).

[15] D Y ZHOU, O BOUSQUET, T N LAL et al. Learning with local and global consistency. British Columbia, 321-328(2003).

[16] C P HOU, F P NIE, Y Y JIAO et al. Learning a subspace for clustering via pattern shrinking. Information Processing and Management： an International Journal, 49, 871-883(2013).

[17] X J CHANG, F P NIE, Z G MA et al. A convex formulation for spectral shrunk clustering. ArXiv e-Prints(2014).

[18] X W ZHAO, F P NIE, R WANG et al. Robust fuzzy K-means clustering with shrunk patterns learning. IEEE Transactions on Knowledge and Data Engineering, 1(2021).

[19] K L WU, J YU, M S YANG. A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests. Pattern Recognition Letters, 26, 639-652(2005).

[20] M J LI, M K NG, Y M CHEUNG et al. Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters. IEEE Transactions on Knowledge and Data Engineering, 20, 1519-1534(2008).