[1] Xudong Tang, Chao Dong, Wei Zhang: Contrastive author-aware text clustering. Pattern Recognit. 130: 108787 (2022)
[2] Junpeng Tan, Zhijing Yang, Yongqiang Cheng, Jielin Ye, Bing Wang, Qingyun Dai: SRAGL-AWCL: A two-step multi-view clustering via sparse representation and adaptive weighted cooperative learning. Pattern Recognit. 117: 107987 (2021)
[3] Hang Zhang, Haili Li, Ning Chen, Shengfeng Chen, Jian Liu: Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation. Pattern Recognit. 121: 108201 (2022)
[4] Guillaume Guenard, Pierre Legendre: Hierarchical Clustering with Contiguity Constraint in R. J. Stat. Softw. 103(7) (2022)
[5] Chunrong Wu, Qinglan Peng, Jia Lee, Kenji Leibnitz, Yunni Xia: Effective hierarchical clustering based on structural similarities in nearest neighbor graphs. Knowl. Based Syst. 228: 107295 (2021)
[6] Jianbo Shi, Jitendra Malik: Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8): 888-905 (2000)
[7] Abhishek Kumar, Hal Daume: A Co-training Approach for Multi-view Spectral Clustering. ICML 2011: 393-400
[8] Jiexing Liu, Chenggui Zhao: Density Gain-Rate Peaks for Spectral Clustering. IEEE Access 9: 46000-46010 (2021)
[9] Ng A. Y, Jordan M. I, Weiss Y, On spectral clustering: Analysis and an algorithm, NIPS'01: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, 849-856 (2001).
[10] Von Luxburg U, A tutorial on spectral clustering, Statistics and Computing, 17(4), 395-416 (2007).
[11] Yessica Nataliani, Miin-Shen Yang: Powered Gaussian kernel spectral clustering. Neural Comput. Appl. 31(S-1): 557-572 (2019)
[12] Zelnik-Manor L, Perona P, Self-tuning spectral clustering, Neural Information Processing Systems (NIPS 2004) Vancouver, British Columbia, Canada, 1601-1608 (2004)
[13] Tong Liu, Jingting Zhu, Jukai Zhou, Yongxin Zhu, Xiaofeng Zhu: Initialization-similarity clustering algorithm. Multim. Tools Appl. 78(23): 33279-33296 (2019)
[14] Xianchao Zhang, Jingwei Li, Hong Yu: Local density adaptive similarity measurement for spectral clustering. Pattern Recognit. Lett. 32(2): 352-358 (2011)
[15] Hassan Motallebi, Rabeeh Nasihatkon, Mina Jamshidi: A Local Mean-based Distance Measure for Spectral Clustering. Pattern Analysis \& Applications 25(2), 351-359 (2022)
[16] Zongqi Cao, Hongjia Chen, Xiang Wang: Spectral clustering based on the local similarity measure of shared neighbors, ETRI Journal, 2022, 44(5): 769-779
[17] Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani: Construction of the similarity matrix for the spectral clustering method: Numerical experiments. J. Comput. Appl. Math. 375: 112795 (2020)
[18] Malgorzata Lucinska, Slawomir T. Wierzchon: Spectral Clustering Based on k-Nearest Neighbor Graph. CISIM 2012: 254-265
[19] Tan M, Zhang S, Wu L, Mutual KNN based spectral clustering, Neural computing and applications, 32, 6435-6442 (2020)
[20] Mashaan A. Alshammari, John Stavrakakis, Masahiro Takatsuka: Refining a k-nearest neighbor graph for a computationally efficient spectral clustering. Pattern Recognit. 114: 107869 (2021)
[21] Yongda Cai, Joshua Zhexue Huang, Jianfei Yin: A new method to build the adaptive k-nearest neighbors similarity graph matrix for spectral clustering. Neurocomputing 493: 191-203 (2022)
[22] Xiucai Ye, Tetsuya Sakurai: Spectral clustering using robust similarity measure based on closeness of shared Nearest Neighbors. IJCNN 2015: 1-8
[23] Tulin Inkaya: A parameter-free similarity graph for spectral clustering. Expert Syst. Appl. 42(24): 9489-9498 (2015)
[24] F. Nie, X. Wang, H. Huang, Clustering and projected clustering with adaptive neighbors, in: Proceedings of the 20th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, 2014, pp. 977–986.
[25] Z. Bian, H. Ishibuchi, S. Wang, Joint learning of spectral clustering structure and fuzzy similarity matrix of data, IEEE Trans. Fuzzy Syst. 27 (1) (2018) 31–44
[26] K.K. Sharma, A. Seal, Spectral embedded generalized mean based k-nearest neighbors clustering with s-distance, Expert Syst. Appl. 169 (2021) 114326.
[27] Ufuk Bahceci: New bounds for the empirical robust Kullback-Leibler divergence problem. Inf. Sci. 637: 118972 (2023)
[28] Jon Louis Bentley: Multidimensional Binary Search Trees Used for Associative Searching. Commun. ACM 18(9): 509-517 (1975)
[29] F. Pedregosa, et al. Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.
[30] P. Fanti, and S. Sieranoja, “K-Means Properties on Six Clustering Benchmark Datasets,” Appl. Intell., vol. 48, no. 12, pp. 4743-4759, 2018.
[31] D. Dua, and C. Graff, “UCI Machine Learning Repository,” http://archive.ics.uci.edu/ml, 2019.
[32] J. B. MacQueen, "Some methods for classification and analysis of multivariate observations,” Proc. 5th Berkeley Symp. Math. Statist. Prob., pp. 281–297, 1967.
[33] C. Malzer, and M. Baum, “A Hybrid Approach to Hierarchical Density-based Cluster Selection," Proc. IEEE Int. Conf. Multisens. Fusion Integr. Intell. Syst., pp. 223-228, 2020.
[34] A. Bryant, and K. J. Cios, "RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates," IEEE Trans. Knowl. Data Eng., vol. 30, no. 6, pp. 1109-1121, 2018.
[35] M. S. Sarfraz, V. Sharma, and R. Stiefelhagen, “Efficient Parameter-Free Clustering Using First Neighbor Relations," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 8934-8943, 2019.