[1]李凯,高岩,曹喆.自动调整样本和特征权值的模糊聚类算法[J].哈尔滨工程大学学报,2018,39(09):1554-1560.[doi:10.11990/jheu.201704029]
 LI Kai,GAO Yan,CAO Zhe.Fuzzy clustering algorithm based on the automatic variable weights of samples and features[J].hebgcdxxb,2018,39(09):1554-1560.[doi:10.11990/jheu.201704029]
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自动调整样本和特征权值的模糊聚类算法(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
39
期数:
2018年09期
页码:
1554-1560
栏目:
出版日期:
2018-09-05

文章信息/Info

Title:
Fuzzy clustering algorithm based on the automatic variable weights of samples and features
作者:
李凯 高岩 曹喆
河北大学 网络空间安全与计算机学院, 河北 保定 071002
Author(s):
LI Kai GAO Yan CAO Zhe
School of Cyber Security and Computer, Hebei University, Baoding 071002, China
关键词:
模糊聚类目标函数样本与特征加权样本加权特征加权核方法特征噪声样本噪声
分类号:
TP391
DOI:
10.11990/jheu.201704029
文献标志码:
A
摘要:
针对模糊c均值聚类算法对特征噪声和样本噪声较敏感的缺陷,依据特征和样本对聚类的不同影响,将特征权值和样本权值引入到模糊c均值聚类的目标函数,并获得了一个模糊聚类模型。利用拉格朗日方法对该模型求解,提出了样本和特征权值自动调整的模糊聚类算法;同时,将核策略引入到该模糊聚类模型,提出了样本和特征权值自动调整的核模糊聚类算法。实验结果表明该方法对含有特征噪声与样本噪声数据的聚类具有较好的处理能力,为特征提取与样本选取等问题提供了一种可行的途径。

参考文献/References:

[1] 李衡, 康维新. 基于EMD与模糊聚类的桩缺陷特征提取与识别[J].应用科技, DOI:10.11991/yykj.201803015. LI Heng, KANG Weixin. Pile defect feature extraction and identification based on Empirical Mode Decomposition (EMD) and fuzzy clustering[J]. Applied science and technology, DOI:10.11991/yykj.201803015.
[2] 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.BIAN Zekang,WANG Shitong.Robust FCM clustering algorithm based on hybrid-distance learning[J]. CAAI transactions on intelligent systems,2017,12(4):450-458.
[3] ZHOU Jin, CHEN Long, CHEN C L P, et al. Fuzzy clustering with the entropy of attribute weights[J]. Neurocomputing, 2016,198:125-134.
[4] FAHAD A, ALSHATRI N, TARI Z, et al. A survey of clustering algorithms for big data:taxonomy and empirical analysis[J].IEEE transactions on emerging topics in computing, 2014, 2(3):267-279.
[5] TIMOTHY C H, JAMES C B, CHRISTOPHER L,et al. Fuzzy c-means algorithms for very large data[J]. IEEE transactions on fuzzy systems, 2012,20(6):1130-1146.
[6] 朱林,王士同,邓赵红. 改进模糊划分的FCM聚类算法的一般化研究[J].计算机研究与发展,2009,46(5):814-822.ZHU Lin,WANG Shitong,DENG Zhaohong. Research on generalized fuzzy c-means clustering algorithm with improved fuzzy partitions[J]. Journal of computer research and development,2009,46(5):814-822.
[7] WU L, STEVEN 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.
[8] STEFAN F, FRIEDHELM S. Semi-supervised clustering of large data sets with kernel methods[J]. Pattern recognition letters, 2014, 37:78-84.
[9] DESARBO W S, CARROLL J D, CLARK L A, et al. Synthesized clustering:a method for amalgamating alternative clustering bases with differential weighting of variables[J]. Psychometrika, 1984, 49(1):57-78.
[10] MAKARENKOV V, LEGENDRE P. Optimal variable weighting for ultrametric and additive trees and k-means partitioning:methods and software[J]. Journal of classification, 2001, 18(2):245-271.
[11] MODHA D S, SPANGLER W. S. Feature weighting in k-means clustering[J]. Machine learning, 2003, 52(3):217-237.
[12] HUANG J Z, NG M K, RONG Hongqiang, et al. Automated variable weighting in K-means type clustering[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(5):657-668.
[13] FERREIRA M R P, DE A T D, CARVALHO F. Kernel-based hard clustering methods in the feature space with automatic variable weighting[J]. Pattern recognition, 2014, 47(9):3082-3095.
[14] FERREIRA M R P, DE A T DE CARVALHO F, SIM? ES E C. Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables[J]. Pattern recognition, 2016, 51:310-321.
[15] 李洁, 高新波, 焦李成. 基于特征加权的模糊聚类新算法[J]. 电子学报, 2006, 34(1):89-92. LI Jie, GAO Xinbo, JIAO Licheng. A new feature weighted fuzzy clustering algorithm[J]. Acta electronica sinica, 2006, 34(1):89-92.
[16] LI Jie, GAO Xinbo, JIAO Licheng. A novel typical-sample-weighted clustering algorithm for large data sets[C]//Computational Intelligence and Security. Heidelberg, 2005, 3801:696-703.
[17] YU Jian, YANG Miinshen, LEE E S. Sample-weighted clustering methods[J]. Computers & mathematics with applications, 2011, 62(5):2200-2208.
[18] 刘兵, 夏士雄, 周勇, 等. 基于样本加权的可能性模糊聚类算法[J]. 电子学报, 2012, 40(2):371-375. LIU Bing, XIA Shixiong, ZHOU Yong, et al. A sample-weighted possibilistic fuzzy clustering algorithm[J]. Acta electronica sinica, 2012, 40(2):371-375.
[19] WEN Wudi, LIU Zhongle, LI Hua. A method to ascertain parameters of samples and their feature weights in the weighted fuzzy clustering[J]. Applied mechanics and materials, 2013, 300-301:653-658.
[20] PIMENTEL B A, DE SOUZA R M C R. Multivariate fuzzy c-means algorithms with weighting[J]. Neurocomputing, 2016, 174:946-965.
[21] SIMINSKI K. Fuzzy weighted c-ordered means clustering algorithm[J]. Fuzzy sets and systems, 2017, 318:1-33.
[22] ZHU Xiubin, PEDRYCZ W, LI Zhiwu. Fuzzy clustering with nonlinearly transformed data[J]. Applied soft computing, 2017, 61:364-376.
[23] CHAGHARI A, FEIZI-DERAKHSHI M R, BALAFAR M A. Fuzzy clustering based on Forest optimization algorithm[J]. Journal of King Saud university-computer and information sciences, 2018, 30(1):25-32.
[24] DUA D,KARRA E. UCI Machine Learning Repository[EB/OL]. Irvine, CA:University of California. Department of Information and Computer Science, 2017. http://archive.ics.uci.edu/ml.

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备注/Memo

备注/Memo:
收稿日期:2017-4-12。
基金项目:国家自然科学基金项目(61375075);河北省自然科学基金项目(F2018201060);河北大学自然科学基金项目(799207217074).
作者简介:李凯(1963-),男,教授.
通讯作者:李凯,E-mail:likai@hbu.edu.cn
更新日期/Last Update: 2018-09-12