To weaken the influence of the insufficient datasets and noises on the clustering analysis, a clustering algorithm, transfer generalized fuzzy C-means with improved fuzzy partitions (T-GIFP-FCM) is proposed based on the FCM framework-based clustering algorithm GIFP-FCM. By leveraging the historical knowledge in the related scene (domain) , the performance of T-GIFP-FCM is enhanced. Even if the data in the current scene are not enough, the promising clustering results can be obtained. The experimental results show the proposed algorithm has better performance compared with the traditional algorithms in situations of insufficient data.
蒋亦樟，邓赵红，王骏，葛洪伟，王士同. 基于知识利用的迁移学习一般化增强模糊划分聚类算法[J]. 模式识别与人工智能, 2013, 26(10): 975-984.
JIANG Yi-Zhang, DENG Zhao-Hong, WANG Jun, GE Hong-Wei, WANG Shi-Tong. Transfer Generalized Fuzzy C-Means Clustering Algorithm with Improved Fuzzy Partitions by Leveraging Knowledge. , 2013, 26(10): 975-984.
[1] Hall L O, Goldgof D B. Convergence of the Single-Pass and Online Fuzzy C-Means Algorithms. IEEE Trans on Fuzzy Systems, 2011, 19(4): 792-794 [2] Wu K L, Yang M S. Alternative C-Means Clustering Algorithms. Pattern Recognition, 2002, 35(10): 2267-2278 [3] Yang M S. On a Class of Fuzzy Classification Maximum Likelihood Procedures. Fuzzy Sets and Systems, 1993, 57(3): 365-375 [4] Lin J S. Fuzzy Clustering Using a Compensated Fuzzy Hopfield Network. Neural Processing Letters, 1999, 10(1): 35-48 [5] Krishnapuram R, Keller J M. A Possibilistic Approach to Clus-tering. IEEE Trans on Fuzzy Systems, 1993, 1(2): 98-110 [6] Krishnapuram R, Keller J M. The Possibilistic Means Algorithms: Insights and Recommendation. IEEE Trans on Fuzzy Systems, 1996, 4(3): 385-393 [7] Li Ruiping, Mukaidon M. A Maximum-Entropy Approach to Fuzzy Clustering // Proc of the 4th IEEE International Conference on Fuzzy System. Yokohama, Japan, 1995, IV: 2227-2232 [8] Karayiannis N B. MECA: Maximum Entropy Clustering Algorithm // Proc of the 3rd IEEE International Conference on Fuzzy Systems. Orlando, USA, 1994: 630-635 [9] Cwe I, Fahn C. The Multisynapse Neural Network and Its Application to Fuzzy Clustering. IEEE Trans on Neural Networks, 2002, 13(3): 600-618 [10] Deng Zhaohong, Wang Shitong, Wu Xisheng, et al. Robust Maximum Entropy Clustering Algorithm RMEC and Its Outlier Labeling. Engineering Science, 2004, 6(9): 38-45 (in Chinese) (邓赵红,王士同,吴锡生,等.鲁棒的极大熵聚类算法RMEC及其例外点标识.中国工程科学, 2004, 6(9): 38-45) [11] Pan Jialin, Yang Qiang. A Survey on Transfer Learning. IEEE Trans on Knowledge and Data Engineering, 2010, 22(10): 1345-1359 [12] Xue Guirong, Dai Wenyuan, Yang Qiang, et al. Topic-Bridged PLSA for Cross-Domain Text Classification // Proc of the 31st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA, 2008: 627-634 [13] Pan J S, Tsang I W, Kwok J T, et al. Domain Adaptation via Transfer Component Analysis // Proc of the 21st International Joint Conference on Artificial Intelligence. San Francisco, USA, 2009: 1187-1192 [14] Jiang Yizhang, Deng Zhaohong, Wang Shitong. Mamdani-Larsen Type Transfer Learning Fuzzy System. Acta Automatica Sinica, 2012, 38(9): 1393-1409 (in Chinese) (蒋亦樟,邓赵红,王士同.ML型迁移学习模糊系统.自动化学报, 2012, 38(9): 1393-1409) [15] Tao Jianwen, Chung F L, Wang Shitong. On Minimum Distribution Discrepancy Support Vector Machine for Domain Adaptation. Pattern Recognition, 2012, 45(11): 3962-3984 [16] Duan Lixin, Tsang I W, Xu Dong. Domain Transfer Multiple Kernel Learning. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(3): 465-479 [17] Gao Xinbo, Wang Xiumei, Li Xuelong, et al. Transfer Latent Va-riable Model Based on Divergence Analysis. Pattern Recognition, 2011, 44(10/11): 2358-2366 [18] Cao Xianbin, Wang Zhong, Yan Pingkun, et al. Transfer Learning for Pedestrian Detection. Neurocomputing, 2013, 100: 51-57 [19] Yu Chongchong, Tian Rui, Tan Li, et al. Integrated Transfer Learning Algorithmic for Unbalanced Samples Classification. Acta Electronica Sinica, 2012, 40(7): 1358-1363 (in Chinese) (于重重,田 蕊,谭 励,等.非平衡样本分类的集成迁移学习算法.电子学报, 2012, 40(7): 1358-1363) [20] Seok H S, Hwang K B, Zhang B T. Feature Relevance Network-Based Transfer Learning for Indoor Location Estimation. IEEE Trans on Systems, Man and Cybernetics, 2011, 41(5): 711-719 [21] Hoppner F, Klawonn F. Improved Fuzzy Partitions for Fuzzy Regression Models. International Journal of Approximate Reasoning, 2003, 32(2/3): 85-102 [22] Zhu Lin, Chung F L, Wang Shitong. Generalized Fuzzy C-Means Clustering Algorithm with Improved Fuzzy Partitions. IEEE Trans on Systems, Man and Cybernetics, 2009, 39(3): 578-591 [23] Zhang Yajun, Liu Zhiqiang. Self-Splitting Competitive Learning: A New Online Clustering Paradigm. IEEE Trans on Neural Network, 2002, 13(2): 369-380 [24] Wu Shuanhu, Liew A W, Yan Hong, et al. Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Compe-titive Learning. IEEE Trans on Information Technology in Biomedicine, 2004, 8(1): 5-15 [25] Deng Zhaohong, Choi K S, Chung F L, et al. Enhanced Soft Subspace Clustering Integrating Within-Cluster and Between-Cluster Information. Pattern Recognition, 2010, 43(3): 767-781 [26] Gu Quanquan, Zhou Jie. Learning the Shared Subspace for Multi-Task Clustering and Transductive Transfer Classification // Proc of the 9th IEEE International Conference on Data Mining. Washington, USA, 2009: 159-168 [27] Gu Quanquan, Zhou Jie. Co-Clustering on Manifolds // Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2009: 359-368 [28] Jing Liping, Ng K M, Huang Zhexue. An Entropy Weighting K-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data. IEEE Trans on Knowledge and Data Engineering, 2007, 19(8): 1026-1041 [29] Liu Jun, Mohammed J, Carter J, et al. Distance-Based Clustering of CGH Data. Bioinformatics, 2006, 22(16): 1971-1978
2013, Vol. 26 
