[1]卞则康,王士同.基于混合距离学习的鲁棒的模糊C均值聚类算法[J].智能系统学报,2017,12(4):450-458.[doi:10.11992/tis.201607019]
 BIAN Zekang,WANG Shitong.Robust FCM clustering algorithm based on hybrid-distance learning[J].CAAI Transactions on Intelligent Systems,2017,12(4):450-458.[doi:10.11992/tis.201607019]
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基于混合距离学习的鲁棒的模糊C均值聚类算法

参考文献/References:
[1] 王骏, 王士同. 基于混合距离学习的双指数模糊C均值算法[J]. 软件学报, 2010, 21(8):1878-1888.WANG Jun, WANG Shitong. Double indices FCM algorithm based on hybrid distance metric learning[J]. Journal of software, 2010, 21(8):1878-1888.
[2] WU L, HOI S C H, JIN R, et al. Learning bregman distance functions for semi-supervised clustering[J]. IEEE transactions on knowledge and data engineering, 2012, 24(3):478-491.
[3] WU K L, YANG M S. Alternative c-means clustering algorithms[J]. Pattern recognition, 2002, 35(10):2267-2278.
[4] XING E P, NG A Y, JORDAN M I, et al. Distance metric learning, with application to clustering with side-information[J]. Advances in neural information processing systems, 2003, 15:505-512.
[5] BAR-Hillel A, HERTZ T, SHENTAL N, et al. Learning a mahalanobis metric from equivalence constraints[J]. Journal of machine learning research, 2005, 6(6):937-965.
[6] 郭瑛洁, 王士同, 许小龙. 基于最大间隔理论的组合距离学习算法[J]. 智能系统学报, 2015, 10(6):843-850.
[7] YE J, ZHAO Z, LIU H. Adaptive distance metric learning for clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007:1-7.
[8] WANG X, WANG Y, WANG L. Improving fuzzy c-means clustering based on feature-weight learning[J]. Pattern recognition letters, 2004, 25(10):1123-1132.
[9] HE P, XU X, HU K, et al. Semi-supervised clustering via multi-level random walk[J]. Pattern recognition, 2014, 47(2):820-832.
[10] HOI S C H, LIU W, LYU M R, et al. Learning distance metrics with contextual constraints for image retrieval[C]//IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2006:2072-2078.
[11] 曾令伟,伍振兴,杜文才.基于改进自监督学习群体智能(ISLCI)的高性能聚类算法[J].重庆邮电大学学报:自然科学版, 2016, 28(1):131-137.ZENG Lingwei, WU Zhenxing, DU Wencai. Improved self supervised learning collection intelligence based high performance data clustering approach[J].Journal of Chongqing university of posts and telecommunications:natural science edition,2016, 28(1):131-137.
[12] 程旸,王士同. 基于局部保留投影的多可选聚类发掘算法[J].智能系统学报, 2016, 11(5):600-607.CHENG Yang, WANG Shitong. A multiple alternative clusterings mining algorithm using locality preserving projections[J]. CAAI transactions on intelligent systems,2016, 11(5):600-607.
[13] DUDA R O, HART P E, STORK D G. Pattern classification[M]//Pattern classification. Wiley, 2001:119-131.
[14] MEI J P, CHEN L. Fuzzy clustering with weighted medoids for relational data[J]. Pattern recognition, 2010, 43(5):1964-1974.
[15] HOPPNER F, KLAWONN F. Improved fuzzy partitions for fuzzy regression models[J]. International journal of approximate reasoning, 2003, 32(2/3):85-102.
[16] ZHU L, CHUNG F L, WANG S. Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions[J]. IEEE transactions on systems man and cybernetics part B, 2009, 39(3):578-591.
[17] STREHL A, GHOSH J. Cluster ensembles-a knowledge reuse framework for combining multiple partitions[J]. Journal of machine learning research, 2002, 3(3):583-617.
[18] IWAYAMA M, TOKUNAGA T. Hierarchical Bayesian clustering for automatic text classification[J]. IJCAI, 1996:1322-1327.
[19] RAND W M. Objective criteria for the evaluation of clustering methods[J]. Journal of the american statistical association, 1971, 66(336):846-850.
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备注/Memo

收稿日期:2016-07-23。
基金项目:国家自然科学基金项目(61272210).
作者简介:卞则康,男,1993年生,硕士研究生,主要研究方向为人工智能和模式识别;王士同,男,1964年生,教授,博士生导师,主要研究方向为人工智能与模式识别。发表学术论文近百篇,其中被SCI、EI检索50余篇。
通讯作者:卞则康,E-mail:bianzekang@163.com.

更新日期/Last Update: 2017-08-25
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