Computer Science ›› 2019, Vol. 46 ›› Issue (8): 249-254.doi: 10.11896/j.issn.1002-137X.2019.08.041
• Artificial Intelligence • Previous Articles Next Articles
LUO Xi, FAN Jiu-lun, YU Hai-yan, LIANG Dan
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| [1]BEZDEK J C.Pattern Recognition with fuzzy objective function algorithms[M].New York:Plenum Press,1981. [2]KRISHNAPURAM R,KELLER J M.A possibilistic approach to clustering[J].IEEE Transactions on Fuzzy Systems,1993,1(2):98-110. [3]KRISHNAPURAM R,KELLER J M.The possibilistic C-means algorithm:insights and recommendations[J].IEEE Transactions on Fuzzy Systems,1996,4(3):385-393. [4]TIMM H,BORGELT C,DÖRING C,et al.An extension to possibilistic fuzzy cluster analysis[J].Fuzzy Sets and Systems,2004,147(1):3-16. [5]FERRARO M B,GIORDANI P.On possibilistic clustering with repulsion constraints for imprecise data[J].InformationScie-nces,2013,245:63-75. [6]PAL N R,PAL K,KELLER J M,et al.A Possibilistic Fuzzy c-Means Clustering Algorithm[J].IEEE Transactions on Fuzzy Systems,2005,13(4):517-530. [7]ASKARI S,MONTAZERIN N,FAZEL Z M H,et al.Genera- lized entropy based possibilistic fuzzy C-Means for clustering noisy data and its convergence proof[J].Neurocomputing,2017,219:186-202. [8]SARKAR J P,SAHA I,MAULIK U.Rough Possibilistic Type-2 Fuzzy C-Means clustering for MR brain image segmentation[J].Applied Soft Computing,2016,46:527-536. [9]XIE Z P,WANG S T,CHUNG F L.An enhanced possibilistic c-means clustering algorithm EPCM[J].Soft Computing,2008,12(6):593-611. [10]HAMASUNA Y,ENDO Y.Sequential Extraction by Using Two Types of Crisp Possibilistic Clustering[C]∥Proceedings ofIEEE International Conference on Systems,Man,and Cybernetics.New York:IEEE Press,2013:3505-3510. [11]XENAKI S D,KOUTROUMBAS K D,RONTOGIANNIS A A.Sparsity-Aware Possibilistic Clustering Algorithms[J].IEEE Transactions on Fuzzy Systems,2016,24(4):1611-1626. [12]KOUTROUMBAS K D,XENAKI S D,RONTOGIANNIS A A.On the Convergence of the Sparse Possibilistic C-Means Algorithm[J].IEEE Transactions on Fuzzy Systems,2018,26(1):324-337. [13]YU H Y,FAN J L.Cutset-type possibilistic c-means clustering algorithm[J].Applied Soft Computing,2018,64:401-422. [14]PEDRYCZ W.Shadowed sets:representing and processing fuzzy sets[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),1998,28(1):103-109. [15]PEDRYCZ W.From fuzzy sets to shadowed sets:interpretation and computing[J].International Journal of Intelligent Systems,2009,24(1):48-61. [16]ZHOU J,PEDRYCZ W,MIAO D Q.Shadowed sets in the cha-racterization of rough-fuzzy clustering[J].Pattern Recognition,2011,14(8):1738-1749. [17]ZHANG K,KONG W R,LIU P P,et al.Partition region-based suppressed fuzzy C-means algorithm[J].Journal of Systems Engineering and Electronics,2017,28(5):996-1008. [18]WANG H L,SHE K,ZHOU M T.Shadowed Sets-based Rough Fuzzy Possibilistic C-means Clustering[J].Computer Science,2013,40(1):191-194.(in Chinese) 汪海良,佘堃,周明天.基于阴影集的粗糙模糊可能性C均值聚类算法[J].计算机科学,2013,40(1):191-194. |
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