[1] Hathaway R J, Bezdek J C. Fuzzy C-means clustering of incomplete data[J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, 2001, 31(5): 735-744.
[2] Gu B, Sun X M, Sheng V S. Structural minimax probability machine[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(7): 1646-1656.
[3] Song K, Feng X, Yu H Q. New fuzzy clustering based on group evolution strategies[J]. Computer Engineering and Applications, 2018, 54(4): 36-43.宋凯, 冯翔, 虞慧群. 基于群进化策略的模糊聚类算法[J].计算机工程与应用, 2018, 54(4): 36-43.
[4] Deb R, Liew A W. Missing value imputation for the analysis of incomplete traffic accident data[J]. Information Sciences, 2016, 339: 274-289.
[5] Marek ?, Lukasz S, Jacek T, et al. Generalized RBF kernel for incomplete data[J]. Knowledge-Based Systems, 2019, 173: 150-162.
[6] Tucker J, Lyndsay S, John R. Handling missing data in self-exciting point process models[J]. Spatial Statistics, 2019, 29: 160-176.
[7] Jia H N, Wang S T. Reinforced rule-based fuzzy models for noisy data and its implementation[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(11): 120-131.贾海宁, 王士同. 面向噪声整数据的强化模糊规则模型及实现[J]. 计算机科学与探索, 2018, 12(11): 120-131.
[8] Qian X D, Luo Y F. Incomplete data clustering algorithm based on mutual information attributes ranking[J]. Information and Control, 2019, 48(1): 80-87.钱晓东, 罗彦福. 基于互信息属性排序的不完整数据聚类算法[J]. 信息与控制, 2019, 48(1): 80-87.
[9] Shao X C. Research on key technique of mixed data clus-tering based on sparse representation[D]. Beijing: University of Science & Technology Beijing, 2018: 50-72.邵晓晨. 基于稀疏表示的混合属性数据聚类关键技术研究[D]. 北京: 北京科技大学, 2018: 50-72.
[10] Zhang L, Pan H, Wang B L, et al. Interval fuzzy C-means approach for incomplete data clustering based on neural networks[J]. Journal of Internet Technology, 2018, 19(4): 1089-1098.
[11] Li T H, Zhang L Y, Lu W, et al. Interval kernel fuzzy C-means clustering of incomplete data[J]. Neurocomputing, 2017, 237: 316-331.
[12] Thanh N, Trong H, Pedrycz W. Towards interval-valued fuzzy set-based collaborative fuzzy clustering algorithms[J]. Pattern Recognition, 2018, 81: 404-416.
[13] Jin H, Qian X Z. Optimized density peak clustering algorithm by natural nearest neighbor[J]. Journal of Frontiers of Com-puter Science and Technology, 2019, 13(4): 711-720.金辉, 钱雪忠. 自然最近邻优化的密度峰值聚类算法[J]. 计算机科学与探索, 2019, 13(4): 711-720.
[14] Tang Y M, Hu X H, Pedrycz W, et al. Possibilistic fuzzy clustering with high-density viewpoint[J]. Neurocomputing, 2019, 329: 407-423.
[15] Liu R H, Huang W P, Fei Z S, et al. Constraint-based clu-stering by fast search and find of density peaks[J]. Neuro-computing, 2019, 330: 223-237.
[16] Antonio I, Rosanna V, Francisco C. Fuzzy clustering of distributional data with automatic weighting of variable components[J]. Information Sciences, 2017, 406: 248-268.
[17] Zhang L Y, Pedrycz W, Lu W, et al. An interval weighted fuzzy C-means clustering by genetically guided alternating optimization[J]. Expert Systems with Applications, 2014, 41(13): 5960-5971.
[18] Kishore K R, Lokendra B, Sreenivasa R. A robust unsu-pervised pattern discovery and clustering of speech signals [J]. Pattern Recognition Letters, 2018, 116: 254- 261.
[19] Wang J, Zhao F. Multi-objective evolutionary fuzzy clustering algorithm based on semi-supervision[J]. Computer Engin-eering and Applications, 2017, 53(22): 40-44.王俊, 赵凤. 基于半监督的多目标进化模糊聚类算法[J]. 计算机工程与应用, 2017, 53(22): 40-44.
[20] Guo J, Yuan X, Han C. Sensor selection based on maximum entropy fuzzy clustering for target tracking in large-scale sensor networks[J]. IET Signal Processing, 2017, 11(5): 613-621.
[21] Liu W, Gu W, Sheng W X, et al. Virtual cluster control for active distribution system using pinning-based distributed secondary control[J]. Electrical Power and Energy Systems, 2019, 109: 710-718.
[22] Moufid B, Mouna G, Kheireddine C. Interval-valued mem-bership function estimation for fuzzy modeling[J]. Fuzzy Sets and Systems, 2019, 61: 101-113.
[23] Wang X, Yu F S, Pedryczd W, et al. Clustering of interval-valued time series of unequal length based on improved dynamic time warping[J]. Expert Systems with Applications, 2019, 125: 293-304.
[24] Bugata P, Drotar P. Weighted nearest neighbors feature selection[J]. Knowledge-Based Systems, 2019, 163: 749-761.
[25] Xia P, Zhang L, Li F. Learning similarity with cosine similarity ensemble[J]. Information Sciences, 2015, 307(1):39-52.
[26] Jiang Z, Li T, Min W. Fuzzy C-means clustering based on weights and gene expression programming[J]. Pattern Rec-ognition Letters, 2017, 90(1): 1-7.
[27] Wang L. Research of fuzzy clustering algorithm for incom-plete data based on the improved ACO with interval superv-ision[D]. Shenyang: Liaoning University, 2016.王鹭. 改进蚁群优化的区间监督模糊聚类算法研究[D]. 沈阳: 辽宁大学, 2016.
[28] Zhang L, Li B X, Zhang L Y, et al. Fuzzy clustering of incom-plete data based on missing attribute interval size[C]//Pro-ceedings of the 2015 IEEE International Conference on Anti- counterfeiting, Security, and Identification, Xiamen, Sep 25-27, 2015. Piscataway: IEEE, 2015: 101-104.
[29] Liu Y. Research of fuzzy clustering algorithm for incomplete data based on information feedback RBF network valuation[D]. Shenyang: Liaoning University, 2018.孙颖. 局部加权的不完整数据混杂聚类算法研究[D]. 沈阳: 辽宁大学, 2017.
[30] Sun Y. Research on hybird clustering algorithm for incomplete data based on local weighting[D]. Shenyang: Liaoning Uni-versity, 2017.刘洋. 信息反馈RBF网络估值的不完整数据模糊聚类算法研究[D]. 沈阳: 辽宁大学, 2018. |