[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]
点击复制
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]
基于混合距离学习的鲁棒的模糊C均值聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第4期
页码:
450-458
栏目:
学术论文—机器学习
出版日期:
2017-08-25
- Title:
- Robust FCM clustering algorithm based on hybrid-distance learning
- Keywords:
- distance metric; FCM clustering algorithm; pairwise constraints; side information; hybrid distance; semi-supervised; GIFP-FCM; robustness
- 分类号:
- TP181
- 摘要:
- 距离度量对模糊聚类算法FCM的聚类结果有关键性的影响。实际应用中存在这样一种场景，聚类的数据集中存在着一定量的带标签的成对约束集合的辅助信息。为了充分利用这些辅助信息，首先提出了一种基于混合距离学习方法，它能利用这样的辅助信息来学习出数据集合的距离度量公式。然后，提出了一种基于混合距离学习的鲁棒的模糊C均值聚类算法（HR-FCM算法），它是一种半监督的聚类算法。算法HR-FCM既保留了GIFP-FCM（Generalized FCM algorithm with improved fuzzy partitions）算法的鲁棒性等性能，也因为所采用更为合适的距离度量而具有更好的聚类性能。实验结果证明了所提算法的有效性。
- Abstract:
- The distance metric plays a vital role in the fuzzy C-means clustering algorithm. In actual applications, there is a practical scenario in which the clustered data have a certain amount of side information, such as pairwise constraints with labels. To sufficiently utilize this side information, first, we propose a learning method based on hybrid distance, in which side information can be utilized to attain a distance metric formula for the data set. Next, we propose a robust fuzzy C-means clustering algorithm (HR-FCM algorithm) based on hybrid-distance learning, which is semi-supervised. The HR-FCM inherits the robustness of the GIFP-FCM (generalized FCM algorithm with improved fuzzy partitions) and has better clustering performance due to the more appropriate distance metric. The experimental results confirm the effectiveness of the proposed algorithm.
- 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.
- 相似文献/References:
-
[1]许腾腾,王瑞,黄恒君.一种加入类间因素的曲线聚类算法[J].智能系统学报,2019,14(2):362.[doi:10.11992/tis.201709029]
XU Tengteng,WANG Rui,HUANG Hengjun.Curve clustering algorithms by adding the differences among clusters[J].CAAI Transactions on Intelligent Systems,2019,14():362.[doi:10.11992/tis.201709029]
[2]靳少卫,刘华平,王博文,等.开放环境下未知材质的识别技术[J].智能系统学报,2020,15(5):1020.[doi:10.11992/tis.201903026]
JIN Shaowei,LIU Huaping,WANG Bowen,et al.Recognition of unknown materials in an open environment[J].CAAI Transactions on Intelligent Systems,2020,15():1020.[doi:10.11992/tis.201903026]
[3]罗敏霞,徐东辉.区间值模糊推理的逻辑度量空间[J].智能系统学报,2023,18(3):613.[doi:10.11992/tis.202110019]
LUO Minxia,XU Donghui.Logical metric spaces for interval-valued fuzzy reasoning[J].CAAI Transactions on Intelligent Systems,2023,18():613.[doi:10.11992/tis.202110019]
[4]蔡鸿顺,张琼敏,龙颖.面向阿尔茨海默症辅助诊断的多尺度域适应网络[J].智能系统学报,2023,18(5):1090.[doi:10.11992/tis.202205050]
CAI Hongshun,ZHANG Qiongmin,LONG Ying.Multiscale domain adaptation network for the auxiliary diagnosis of Alzheimer’s disease[J].CAAI Transactions on Intelligent Systems,2023,18():1090.[doi:10.11992/tis.202205050]
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