半监督聚类的若干新进展<sup>*</sup>

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摘要半监督聚类方法利用少量标记数据提高聚类算法的性能，已逐渐发展成为模式识别及相关领域的研究热点.文中首先综述了半监督聚类算法的一些新进展，包括基于约束的方法、基于距离的方法和基于距离与约束的融合方法.然后提出一种基于约束的半监督模糊C-means聚类算法.实验表明，该算法与传统的模糊C-means及半监督K-means方法相比，具有更好的聚类精度.

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	李昆仑
	曹铮
	曹丽苹
	张超
	刘明

关键词 ：半监督聚类, 模糊C-均值(FCM), 标记数据, 无标记数据

Abstract：Small amount of labeled data are used in semi-supervised clustering algorithms to improve the performance of the algorithms. It is a research hotspot in pattern recognition and its related fields. In this paper, some developments on semi-supervised clustering are introduced including constraint-based, distance-based and the combination of them. Using semi-supervised strategy to fuzzy C-means, a semi-supervised fuzzy C-means (constrained FCM) algorithm is proposed. Experimental results show that the proposed method obtains better accuracy compared with FCM and semi-supervised K-means.

Key words： Semi-Supervised Clustering Fuzzy C-Means (FCM) Labeled Data Unlabeled Data

收稿日期: 2008-12-15

ZTFLH:

TP181

基金资助:国家自然科学基金项目(No.60773062, 60873100)、河北省自然科学基金项目(No.F2009000215)和河北省教育厅科研计划项目(No.2008312)资助

作者简介: 李昆仑，男，1962年生，博士，副教授，主要研究方向为模式识别、人工智能、数据挖掘等.E-mail: likunlun@hbu.edu.cn.曹铮，男，1983年生，硕士研究生，主要研究方向为模式识别.曹丽苹，女，1980年生，讲师，硕士研究生，主要研究方向为信息系统.张超，男，1983年生，硕士研究生，主要研究方向为人脸检测.刘明，男，1972年生，讲师，博士，主要研究方向为生物特征识别、图像处理.

引用本文:

李昆仑，曹铮，曹丽苹，张超，刘明. 半监督聚类的若干新进展^*[J]. 模式识别与人工智能, 2009, 22(5): 735-742. LI Kun-Lun, CAO Zheng, CAO Li-Ping, ZHANG Chao, LIU Ming. Some Developments on Semi-Supervised Clustering. , 2009, 22(5): 735-742.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2009/V22/I5/735

[1] Olivier C, Bernhard S, Alexander Z. Semi-Supervised Learning. Cambridge, USA: MIT Press, 2006: 3-10
[2] Blum A, Mitchell T. Combining Labeled and Unlabeled Data with Co-Training // Proc of the 11th Annual Conference on Computational Learning Theory. Madison, USA, 1998: 92-100
[3] Zhong Shi. Semi-Supervised Model-Based Document Clustering: A Comparative Study. Machine Learning, 2006, 65(1): 3-29
[4] Wagstaff K, Cardie C, Rogers S, et al. Constrained K-means Clustering with Background Knowledge // Proc of 18th International Conference on Machine Learning. San Francisco, USA, 2001: 577-584
[5] Wagstaff K, Cardie C. Clustering with Instance-Level Constraints // Proc of the 17th International Conference on Machine Learning. San Francisco, USA, 2000: 1103-1110
[6] Huang Desheng, Pan Wei. Incorporating Biological Knowledge into Distance-Based Clustering Analysis of Micro Array Gene Expression Data. Bioinformatics, 2006, 22(10): 1259-1268
[7] Tari L, Baral C, Kim S. Fuzzy C-Means Clustering with Prior Biological Knowledge. Journal of Biomedical Informatics, 2009, 42(1): 74-81
[8] Ceccarelli M, Maratea A. Improving Fuzzy Clustering of Biological Data by Metric Learning with Side Information. International Journal of Approximate Reasoning, 2008, 47(1): 45-57
[9] Huang Ruizhang, Lam W. An Active Learning Framework for Semi-Supervised Document Clustering with Language Modeling. Data & Knowledge Engineering, 2008, 68(1): 49-67
[10] Erman J, Mahanti A, Arlitt M, et al. Offline/Realtime Traffic Classification Using Semi-Supervised Learning. Performance Evaluation, 2007, 64(9/10/11/12): 1194-1213
[11] Chang Hong, Yeung D Y. Locally Linear Metric Adaptation with Application to Semi-Supervised Clustering and Image Retrieval. Pattern Recognition, 2006, 39(7): 1253-1264
[12] Bensaid A M, Hall L O, Bezdek J C. Partially Supervised Clustering for Image Segmentation. Pattern Recognition, 1996, 29(5): 859-871
[13] Demiriz A, Bennett K P, Embrechts M J. Semi-Supervised Clustering Using Genetic Algorithms // Proc of the Artificial Neural Networks in Engineering Conference. New York, USA, 1999: 809-814
[14] Basu S, Banerjee A, Mooney R J. Semi-Supervised Clustering by Seeding // Proc of the 19th International Conference on Machine Learning. Sydney, Australia, 2002: 19-26
[15] Xing E P, Ng A Y, Jordan M I, et al. Distance Metric Learning, with Application to Clustering with Side-Information // Proc of the 16th Annual Conference on Neural Information Processing Systems. Cambridge, UK, 2003: 505-512
[16] Klein D, Kamvar S D, Manning C. From Instance-Level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering // Proc of the 19th International Conference on Machine Learning. Sydney, Australia, 2002: 307-314
[17] Ng A Y, Jordan M I, Weiss Y. On Spectral Clustering: Analysis and an Algorithm // Proc of the Neural Information Processing Systems Conference. Vancouver, Canada, 2001: 849-856
[18] Kamvar S D, Klein D, Manning C. Spectral Learning // Proc of 18th International Joint Conference on Artificial Intelligence. Acapulco, Mexico, 2003: 561-566
[19]Bilenko M, Basu S, Mooney R J. Integrating Constraints and Metric Learning in Semi-Supervised Clustering // Proc of the 21st International Conference on Machine Learning. Banff, Canada, 2004: 81-88
[20] Basu S, Bilenko M, Mooney R J. A Probabilistic Framework for Semi-Supervised Clustering // Proc of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2004: 59-68
[21] Kulis B, Basu S, Mooey R. Semi-Supervised Graph Clustering: A Kernel Approach // Proc of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005: 457-464
[22] Yan Bojun, Domeniconi C. An Adaptive Kernel Method for Semi-Supervised Clustering // Proc of the 17th European Conference on Machine Learning. Berlin, Germany, 2006: 521-532
[23] Pedrycz W, Waletzky J. Fuzzy Clustering with Partial Supervision. IEEE Trans on Systems, Man and Cybernetics, 1997, 27(5): 787-795
[24] Bhanu B, Dong Anlei. Concepts Learning with Fuzzy Clustering and Relevance Feedback // Proc of the 2nd International Workshop on Machine Learning and Data Mining in Pattern Recognition. Leipzig, Germany, 2002: 102-116
[25] Bouchachia A, Pedrycz W. A Semi-Supervised Clustering Algorithm for Data Exploration // Proc of International Fuzzy Systems Association World Congress. Istanbul, Turkey, 2003: 328-337
[26] Demiriz A, Bennett K P, Embrechts M J. A Genetic Algorithm Approach for Semi-Supervised Clustering. Journal of Smart Engineering System Design, 2002, 4: 35-44
[27] Liu Hong, Huang S T. Evolutionary Semi-Supervised Fuzzy Clustering. Pattern Recognition Letters, 2003, 24(16): 3105-3113
[28] Wang Ling, Bo Liefeng, Jiao Licheng. Density-Sensitive Semi-Supervised Spectral Clustering. Journal of Software, 2007, 18(10): 2412-2422 (in Chinese)
(王玲,薄列峰,焦李成.密度敏感的半监督谱聚类.软件学报, 2007, 18(10): 2412-2422)
[29] Yin Xuesong, Hu Enliang, Chen Songcan. Discriminative Semi-Supervised Clustering Analysis with Pairwise Constraints. Journal of Software, 2008, 19(11): 2791-2802 (in Chinese)
(尹学松,胡恩良,陈松灿.基于成对约束的判别型半监督聚类分析.软件学报, 2008, 19(11): 2791-2802)
[30] Xiao Yu, Yu Jian. Semi-Supervised Clustering Based on Affinity Propagation Algorithm. Journal of Software, 2008, 19(11): 2803-2813 (in Chinese)
(肖宇,于剑.基于近邻传播算法的半监督聚类.软件学报, 2008, 19(11): 2803-2813)
[31] Peng Yan, Zhang Daoqiang. Semi-Supervised Canonical Analysis Algorithm. Journal of Software, 2008, 19(11): 2822-2832 (in Chinese)
(彭岩,张道强.半监督典型相关分析算法. 软件学报, 2008, 19(11): 2822-2832)
[32] Jin Jun, Zhang Daoqiang. Semi-Supervised Robust On-line Clustering Algorithm. Journal of Computer Research and Development, 2008, 45(3): 496-502 (in Chinese)
(金骏,张道强.半监督鲁棒联机聚类算法.计算机研究与发展, 2008, 45(3): 496-502)