An optimization approach to partitional data clustering

被引：9
|
作者：
Kim, J. [2 ]
Yang, J. [1 ]
Olafsson, S. [3 ]
机构：
[1] Chonbuk Natl Univ, Dept Ind & Informat Syst Engn, Jeonju 561756, Jeonbuck, South Korea
[2] KOSBI, Seoul, South Korea
[3] Iowa State Univ, Ames, IA USA
来源：
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | 2009年 / 60卷 / 08期
关键词：
optimization-based partitional clustering; scalability; partitioning; K-MEANS; ALGORITHMS;
D O I：
10.1057/jors.2008.195
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
Scalability of clustering algorithms is a critical issue facing the data mining community. One method to handle this issue is to use only a subset of all instances. This paper develops an optimization-based approach to the partitional clustering problem using an algorithm specifically designed for noisy performance, which is a problem that arises when using a subset of instances. Numerical results show that computation time can be dramatically reduced by using a partial set of instances without sacrificing solution quality. In addition, these results are more persuasive as the size of the problem is larger. Journal of the Operational Research Society (2009) 60, 1069-1084. doi:10.1057/jors.2008.195 Published online 8 April 2009
引用
收藏
页码：1069 / 1084
页数：16
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