[1]汪 中,刘贵全,陈恩红.基于模糊K-harmonic means的谱聚类算法[J].智能系统学报,2009,4(02):95-99.
 WANG Zhong,LIU Gui-quan,CHEN En-hong.A spectral clustering algorithm based on fuzzy Kharmonic means[J].CAAI Transactions on Intelligent Systems,2009,4(02):95-99.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年02期
页码:
95-99
栏目:
出版日期:
2009-04-25

文章信息/Info

Title:
A spectral clustering algorithm based on fuzzy Kharmonic means
文章编号:
1673-4785(2009)02-0095-05
作者:
汪  中12 刘贵全12 陈恩红12
1. 中国科学技术大学计算机科学与技术学院,安徽合肥230027;
2.安徽省计算与通讯软件重点实验室,安徽合肥230027
Author(s):
WANG Zhong12 LIU Gui-quan12CHEN En-hong12
1.School of Computer Science, University of Science and Technology of China, Hefei 230027,China;
2. Key Laboratory of Software in Computing and Communication, Hefei 230027, China
关键词:
谱聚类模糊K-harmonic means初始化敏感聚类中心
Keywords:
spectral clusteringfuzzy Kharmonic meansinitialization sensitivitycluster centers
分类号:
TP311
文献标志码:
A
摘要:
谱聚类作为一种有效的方法广泛应用于机器学习.通过分析谱聚类初始化敏感的实质,引入对初值不敏感的模糊Kharmonic means算法来克服这一缺点,提出一种基于模糊Kharmonic means的谱聚类算法(FKHMSC).与传统谱聚类算法以及对初值敏感的Kmeans、FCM算法相比,改进算法不仅可以识别有挑战性的人工数据,并且可以得到稳定的聚类中心和聚类结果,同时提高了聚类的精确度.实验结果表明了该算法的有效性和可行性.
Abstract:
Spectral clustering is an effective method that is widely used in machine learning. After analyzing the essence of initialization sensitivity in spectral clustering, the fuzzy Kharmonic means (FKHM) algorithm was considered to conquer spectral clustering’s shortcomings, then an spectral clustering algorithm based on FKHM was developed. Compared with the traditional spectral algorithm and the fuzzy cmeans (FCM) algorithm, the suggested algorithm is more sensitive to initial values. The suggested algorithm can not only identify challenging artificial data, but also find stable cluster centers and clustering results, considerably improving clustering precision. Experiments showed that it is an effective and feasible way to improve the performance of spectral clustering algorithms.

参考文献/References:

[1]FIEDLER M. Algebraic connectivity of graphs[M]. Praha: Czechoslovak Mathematical Journal, 1973:298305.[2]VERMA D, MEILA M. A comparison of spectral clustering algorithms[R]. University of Washington, 2003.
 [3]FISCHER I, POLAND J. Amplifying the block matrix structure for spectral clustering[C]//Proceedings of the 14th Annual Machine Conference of Belgium and the Netherlands. Manno, Switzerland, 2005:2128.
[4]FOWLKES C, BELONGIE S, CHUNG F, et al. Spectral grouping using the Nystr〖AKo¨〗m method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 26(2):217225.
[5]EKIN A, PANKANTI S, HAMPAPUR A. Initializationindependent spectral clustering with applications to automatic video analysis[C]//Proc of IEEE ICASSP. Montreal, Canada,2004:641644.
[6]SHI J B,MALIK J. Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888905.
 [7]NG A Y,JORDAN M L, WEISS Y. On spectral clustering:analysis and an algorithm[C]//Advances in Neural Information Processing Systems. Cambridge: MIT Press,2002:897856.
[8]赵 恒,杨万海,张高煜.模糊KHarmonic Means聚类算法[J].西安电子科技大学学报,2005,32(4):603606.
ZHAO Heng, YANG Wanhai, ZHANG Gaoyu. Fuzzy Kharmonic means clustering algorithm[J].Journal of XiDian University,2005,32(4):603606
 [9]ZHANG B,HSU M,DAYAL U. Kharmonic means—a data clustering algorithm[EB/OL].[20060112].http://hpc.isti.cnr.it/~palmeri/datam/articles/HPL1999124.pdf.
[10]HANDL J,KNOWLES J. An evolutionary approach to multiobjective clustering[J]. IEEE Transactions on Evolutionary Computation,2007,11(1):5676.

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备注/Memo

备注/Memo:
收稿日期:2008-12-16.
基金项目:国家自然科学基金资助项目(60775037);教育部新世纪优秀人才支持计划资助项目(NCET050549)
作者简介:汪 中,男,1984年生,硕士研究生,主要研究方向为数据挖掘、机器学习. 
刘贵全,男,1970年生,副教授,博士,主要研究方向为数据挖掘、人工智能、网络安全等.2003年获安徽省科技成果三等奖.发表学术论文50余篇.
陈恩红,男,1968年生,教授,博士生导师,主要研究方向为数据挖掘与机器学习、网络信息处理等.1995年获中国科学院院长奖学金优秀奖,1996年获中国科学技术大学惠普信息科学青年教师奖,2000年获王宽诚育才奖、安徽省科技进步二等奖 ,2004年获安徽省科技进步三等奖、中国科技大学优秀教学成果二等奖,2005年入选教育部新世纪优秀人才支持计划,2006年获王宽诚育才奖一等奖.发表学术论文90余篇.
 E-mail:wzspb@mail.ustc.edu.cn.
更新日期/Last Update: 2009-05-04