聚类分析研究中的若干问题

被引:195
作者
王骏
王士同
邓赵红
机构
[1] 江南大学数字媒体学院
关键词
聚类分析; 聚类方法; 无监督学习;
D O I
10.13195/j.cd.2012.03.4.wangj.013
中图分类号
TP311.13 [];
学科分类号
1201 ;
摘要
聚类分析是重要的数据挖掘方法,目的是寻找数据集中所包含的簇结构.以往研究工作中聚类分析的一些基本问题始终是人们关注的重点,为此在简要回顾具有代表性的研究成果的基础上,总结了该研究所面临的若干基本问题及解决方法,以期能够对相关研究提供有益的参考.
引用
收藏
页码:321 / 328
页数:8
相关论文
共 22 条
[11]  
Clustering high dimensional data: A graph-based relaxed optimization approach[J] . Information Sciences . 2008 (23)
[12]   A hierarchical clustering algorithm based on the Hungarian method [J].
Goldberger, Jacob ;
Tassa, Tamir .
PATTERN RECOGNITION LETTERS, 2008, 29 (11) :1632-1638
[13]   Rough clustering of sequential data [J].
Kumar, Pradeep ;
Krishna, P. Radha ;
Bapi, Raju. S. ;
De, Supriya Kumar .
DATA & KNOWLEDGE ENGINEERING, 2007, 63 (02) :183-199
[14]   A novel approach to noise clustering for outlier detection [J].
Rehm, Frank ;
Klawonn, Frank ;
Kruse, Rudolf .
SOFT COMPUTING, 2007, 11 (05) :489-494
[15]   Time-focused clustering of trajectories of moving objects [J].
Nanni, Mirco ;
Pedreschi, Dino .
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2006, 27 (03) :267-289
[16]   Locally adaptive metrics for clustering high dimensional data [J].
Domeniconi, Carlotta ;
Gunopulos, Dimitrios ;
Ma, Sheng ;
Yan, Bojun ;
Al-Razgan, Muna ;
Papadopoulos, Dimitris .
DATA MINING AND KNOWLEDGE DISCOVERY, 2007, 14 (01) :63-97
[17]   Robust maximum entropy clustering algorithm with its labeling for outliers [J].
Wang, ST ;
Chung, KFL ;
Deng, ZH ;
Hu, DW ;
Wu, XS .
SOFT COMPUTING, 2006, 10 (07) :555-563
[18]   Feature weighting in k-means clustering [J].
Modha, DS ;
Spangler, WS .
MACHINE LEARNING, 2003, 52 (03) :217-237
[19]  
Concept Decompositions for Large Sparse Text Data Using Clustering[J] . Inderjit S. Dhillon,Dharmendra S. Modha.Machine Learning . 2001 (1)
[20]  
Data clustering[J] . A. K. Jain,M. N. Murty,P. J. Flynn.ACM Computing Surveys (CSUR) . 1999 (3)