[1]杨小兵,何灵敏,孔繁胜.切换回归模型的抗噪音聚类算法[J].智能系统学报,2009,4(06):497-501.[doi:10.3969/j.issn.1673-4785.2009.06.005]
 YANG Xiao-bing,HE Ling-min,KONG Fan-sheng.A noise-resistant clustering algorithm for switching regression models[J].CAAI Transactions on Intelligent Systems,2009,4(06):497-501.[doi:10.3969/j.issn.1673-4785.2009.06.005]
点击复制

切换回归模型的抗噪音聚类算法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年06期
页码:
497-501
栏目:
出版日期:
2009-12-25

文章信息/Info

Title:
A noise-resistant clustering algorithm for switching regression models
文章编号:
1673-4785(2009)06-0497-05
作者:
杨小兵1 何灵敏1 孔繁胜2
1.中国计量学院 计算机系,浙江 杭州 310018; 2.浙江大学 计算机学院,浙江 杭州 310012
Author(s):
YANG Xiao-bing1 HE Ling-min1 KONG Fan-sheng2
1. Department of Computer Science, China Jiliang University, Hangzhou 310018, China; 2. College of Computer Science, Zhejiang University, Hangzhou 310012, China
关键词:
切换回归模型聚类抗噪音聚类算法
Keywords:
switching regression models clustering noise-resistant clustering algorithm
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-4785.2009.06.005
文献标志码:
A
摘要:
对切换回归模型的聚类方法一般都没有考虑到噪音的影响,因此在含有噪音数据的情况下,用这些方法聚类的结果就会出现一定的偏差.为了减弱聚类过程中噪音数据的影响,提出了一种新的具有抵抗噪音能力的聚类算法,称为抗噪音聚类算法.该算法通过将已知数据集划分为非噪音数据集和噪音数据集2个子集,然后对非噪音数据集进行聚类分析,估计出模型的各个参数.通过对噪音数据集和非噪音数据集进行不断地调整,同时不断地修正得到的参数估计值,从而得到对聚类结果的优化.实验表明,抗噪音聚类算法能够有效地克服噪音数据对聚类结果的影响,并估计出优质的参数.
Abstract:
Clustering methods for switching regression models usually neglect the effects of noise. As a result, errors usually exist if clustering is carried out in a noisy environment. In order to overcome the effects of noise, a new clustering algorithm, a noiseresistant clustering algorithm, was proposed. The algorithm partitions the dataset into two subdatasets, a noiseless dataset and a noisy dataset, and then performs clustering analysis on the noiseless dataset to estimate parameters. By continuous simultaneous adjustment of the noisy and noiseless datasets and by continuously revising estimated parameters, the results of clustering were improved. Simulation experiments demonstrated that the algorithm efficiently clusters noisy datasets and can provide good estimates of parameters.

参考文献/References:

[1]HAN Jiawei,KAMBER M. 数据挖掘概念与技术[M]. 范明, 孟小峰,译. 北京:机械工业出版社, 2001: 223-261.
[2]HAMERMESH D S. Wage bargains, threshold effects, and the Phillips curve[J]. Quarterly Journal of Economics, 1970, 84(3):501-517.
[3]QUANDT R E. A new approach to estimating switching regressions[J]. J Amer Statist Ass, 1972, 67(338): 306-310.
[4]QUANDT R E, RAMSEY J B. Estimating mixtures of normal distributions and switching regressions[J]. J Amer Statist Ass, 1978, 73: 730-752.
[5]HOSMER D W. Maximum likelihood estimates of the parameters of a mixture of two regression lines[J]. Communications in Statistics, 1974, 3(10):995-1005.
[6]BEZDEK J C. Pattern recognition with fuzzy objective function algorithms[M]. New York: Plenum Press, 1981:88-94.
[7]HATHAWAY R J, BEZDEK J C. Switching regression models and fuzzy clustering[J]. IEEE Trans on Fuzzy Systems, 1993, 1(3):195-204.
[8]OHTA T, YAMAKAWA A, ICHIHASHI H, et al. Projection pursuit switching regression[C]//Proc of 5th International Conference on Soft Computing. Iizuka, Japan, 1998:775-778.
[9]OHTA T, YAMAKAWA A, ICHIHASHI H,et al. Projection pursuit switching regression for analysis of psychological feelings[J]. Journal of Biomedical Soft Computing and Human Sciences, 1998, 4(1): 15-21.
[10]沈红斌, 王士同, 吴小俊. 离群模糊核聚类算法[J]. 软件学报, 2004, 15(7):1021-1029. 
 SHEN Hongbin, WANG Shitong, WU Xiaojun. Fuzzy kernel clustering with outliers[J].Journal of Software, 2004, 15(7): 1021-1029.
[11]WANG Shitong, JIANG Haifeng, LU Hongjun. A new integrated clustering algorithm GFC and switching regression[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2002, 16(4):433-446.
[12]陆宏钧, 江海峰, 王士同. 关于切换回归的集成模糊聚类算法GFC[J]. 软件学报,2002,13(10):1905-1914. 
LU Hongjun, JIANG Haifeng,WANG Shitong. An integrated fuzzy clustering algorithm GFC for switching regressions[J]. Journal of Software, 2002, 13(10): 1905-1914.

相似文献/References:

[1]季瑞瑞,刘 丁.支持向量数据描述的基因表达数据聚类方法[J].智能系统学报,2009,4(06):544.[doi:10.3969/j.issn.1673-4785.2009.06.013]
 JI Rui-rui,LIU Ding.Improved gene expression data clustering using a support vector domain description algorithm[J].CAAI Transactions on Intelligent Systems,2009,4(06):544.[doi:10.3969/j.issn.1673-4785.2009.06.013]
[2]张秀玲,逄宗鹏,李少清,等.ANFIS的板形控制动态影响矩阵方法[J].智能系统学报,2010,5(04):360.
 ZHANG Xiu-ling,PANG Zong-peng,LI Shao-qing,et al.A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems[J].CAAI Transactions on Intelligent Systems,2010,5(06):360.
[3]李伟,杨晓峰,张重阳,等.复杂网络社团的投影聚类划分[J].智能系统学报,2011,6(01):57.
 LI Wei,YANG Xiaofeng,ZHANG Chongyang,et al.A clustering method for community detection on complex networks[J].CAAI Transactions on Intelligent Systems,2011,6(06):57.
[4]陈岳峰,苗夺谦,李文,等.基于概念的词汇情感倾向识别方法[J].智能系统学报,2011,6(06):489.
 CHEN Yuefeng,MIAO Duoqian,LI Wen,et al.Semantic orientation computing based on concepts[J].CAAI Transactions on Intelligent Systems,2011,6(06):489.
[5]方然,苗夺谦,张志飞.一种基于情感的中文微博话题检测方法[J].智能系统学报,2013,8(03):208.
 FANG Ran,MIAO Duoqian,ZHANG Zhifei.An emotion-based method of topic detection from Chinese microblogs[J].CAAI Transactions on Intelligent Systems,2013,8(06):208.
[6]刘恋,常冬霞,邓勇.动态小生境人工鱼群算法的图像分割[J].智能系统学报,2015,10(5):669.[doi:10.11992/tis.201501001]
 LIU lian,CHANG Dongxia,DENG Yong.An image segmentation method based on dynamic niche artificial fish-swarm algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(06):669.[doi:10.11992/tis.201501001]
[7]刘贝贝,马儒宁,丁军娣.基于密度的统计合并聚类算法[J].智能系统学报,2015,10(5):712.[doi:10.11992/tis.201410028]
 LIU Beibei,MA Runing,DING Jundi.Density-based statistical merging clustering algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(06):712.[doi:10.11992/tis.201410028]
[8]朱书伟,周治平,张道文.融合并行混沌萤火虫算法的K-调和均值聚类[J].智能系统学报,2015,10(6):872.[doi:10.11992/tis.201505043]
 ZHU Shuwei,ZHOU Zhiping,ZHANG Daowen.K-harmonic means clustering merged with parallel chaotic firefly algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(06):872.[doi:10.11992/tis.201505043]
[9]谷飞洋,田博,张思萌,等.基于置换检验的聚类结果评估[J].智能系统学报,2016,11(3):301.[doi:10.11992/tis.201603038]
 GU Feiyang,TIAN Bo,ZHANG Simeng,et al.Statistical evaluation of the clustering results based on permutation test[J].CAAI Transactions on Intelligent Systems,2016,11(06):301.[doi:10.11992/tis.201603038]
[10]王跃,杨燕,王红军.一种基于少量标签的改进迁移模糊聚类[J].智能系统学报,2016,11(3):310.[doi:10.11992/tis.201603046]
 WANG Yue,YANG Yan,WANG Hongjun.An improved transfer fuzzy clustering with few labels[J].CAAI Transactions on Intelligent Systems,2016,11(06):310.[doi:10.11992/tis.201603046]

备注/Memo

备注/Memo:
收稿日期:2009-05-11.
基金项目:国家自然科学基金资助项目(60842009).
作者简介:杨小兵,男,1976年生,博士,副教授,硕士生导师,主要研究方向为数据挖掘、知识工程等,发表学术论文10余篇,其中多篇被SCI、EI检索.
何灵敏,男,1974年生,博士,副教授,硕士生导师,主要研究方向为数据挖掘、机器学习等.
孔繁胜,男,1946年生,教授,博士生导师,主要研究方向为知识工程、数据挖掘、人工智能等,获国家科技进步三等奖1项、省部级科技进步一等奖1项、二等奖和三等奖各2项,1993年起享受国务院特殊津贴.在国内外重要刊物上发表学术论文30余篇,出版专著3部.
更新日期/Last Update: 2010-02-17