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朱二周 等:一种采用新型聚类方法的最佳类簇数确定算法 3101
[8] Rathore P, Ghafoori Z, C. Bezdek JC, Palaniswami M, Leckie C. Approximating Dunn’s cluster validity indices for partitions of
big data. IEEE Trans. on Cybernetics, 2019,49(5):16291641. [doi: 10.1109/TCYB.2018.2806886]
[9] Zhang YJ, Wang WN, Zhang XN, Li Y. A cluster validity index for fuzzy clustering. Information Sciences, 2008,178(4):
12051218. [doi: 10.1016/j.ins.2007.10.004]
[10] Yang Y, Jin F, Mohamed K. Survey of clustering validity evaluation. Application Research of Computers, 2008,25(6):16301632
(in Chinese with English abstract).
[11] Calinski T, Harabasz JA. A dendrite method for cluster analysis. Communications in Statistics, 1974,3(1):127. [doi: 10.1080/
03610927408827101]
[12] Gurrutxag I, Albisua I, Arbelaitz O, Martin JI, Muguerza J, Perez JM, Perona I. SEP/COP: An efficient method to find the best
partition in hierarchical clustering based on a new cluster validity index. Pattern Recognition, 2010,43(10):33643373. [doi: 10.
1016/j.patcog.2010.04.021]
[13] Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1979,
PAMI-1(2):224227. [doi: 10.1109/TPAMI.1979.4766909]
[14] Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics,
1973,3(3):3257. [doi: 10.1080/01969727308546046]
[15] Bandyopadhyay S, Maulik U. Nonparametric genetic clustering: comparison of validity indices. IEEE Trans. on Systems, Man, and
Cybernetics, Part C (Applications and Reviews), 2001,31(1):120125. [doi: 10.1109/5326.923275]
[16] Zhu EZ, Zhang YX, Wen P, Liu F. Fast and stable clustering analysis based on grid-mapping K-means algorithm and new
clustering validity index. Neurocomputing, 2019,363:149170. [doi: 10.1016/j.neucom.2019.07.048]
[17] Zhu XF, Zhang SC, Li YG, Zhang JL, Yang LF, Fang Y. Low-rank sparse subspace for spectral clustering. IEEE Trans. on
Knowledge and Data Engineering, 2019,31(8):15321543. [doi: 10.1109/TKDE.2018.2858782]
[18] Chen YW, Tang SY, Bouguila N, Wang C, Du JX, Li HL. A fast clustering algorithm based on pruning unnecessary distance
computations in DBSCAN for high-dimensional data. Pattern Recognition, 2018,83:375387. [doi: 10.1016/j.patcog.2018.05.030]
[19] Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy C-means clustering algorithm. Computers & Geosciences, 1984,10(2-3):191203.
[doi: 10.1016/0098-3004(84)90020-7]
[20] Qian PJ, Zhao KF, Jiang YZ, Su KH, Deng ZH, Wang ST, Jr RFM. Knowledge-leveraged transfer fuzzy C-means for texture image
segmentation with self-adaptive cluster prototype matching. Knowledge-based Systems, 2017,130:3350. [doi: 10.1016/j.knosys.
2017.05.018]
[21] Arora N, Pandey R. Noise adaptive FCM algorithm for segmentation of MRI brain images using local and non-local spatial
information. In: Mohamed BH, ed. Proc. of the 15th Int’l Conf. on Intelligent Systems Design and Applications. New York: IEEE,
2015. 610617. [doi: 10.1109/ISDA.2015.7489187]
[22] Jia HJ, Ding SF, Xu XZ, Nie R. The latest research progress on spectral clustering. Neural Computing & Applications, 2014,
24(7-8):14771486. [doi: 10.1007/s00521-013-1439-2]
[23] Li Y, Liu XY. A modified spectral clustering algorithm based on density. In: Zu Q, Hu B, eds. Proc. of the 2nd Int’l Conf. on
Human Centered Computing. Berlin: Springer-Verlag, 2016. 901906. [doi: 10.1007/978-3-319-31854-7_97]
[24] Airel PS, José Fco. MT, Jesús A. CO, et al. A new overlapping clustering algorithm based on graph theory. In: Batyrshin I,
González Mendoza M, eds. Proc. of the 11th Mexican Int’l Conf. on Artificial Intelligence. Berlin: Springer-Verlag, 2012. 6172.
[doi: 10.1007/978-3-642-37807-2_6]
[25] Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science, 2014,344(6191):14921496. [doi: 10.1126/
science.1242072]
[26] Xie YJ, Gao HC, Xie WX. Fast peak density search clustering algorithm based on the optimization of K-nearest neighbor.
SCIENTIA SINICA Informations, 2016,46(2):258280 (in Chinese with English abstract). [doi: 10.1360/N112015-00135]
[27] Ji X, Yao S, Zhao P. Relative neighborhood and pruning strategy optimized density peaks clustering algorithm. Acta Automatica
Sinica, 2020,46(3):114 (in Chinese with English abstract). [doi: 10.16383/j.aas.c170612]
[28] Ma CL, Shan H, Ma T. Improved density peaks based clustering algorithm with strategy choosing clustering center automatically.
Computer Science, 2016,43(7):255258 (in Chinese with English abstract). [doi: 10.11896/j.issn.1002-137X.2016.7.046]