approximate pairwise clustering for large data sets via sampling plus extension
文献类型：期刊论文
| 作者 | Wang Liang ; Leckie Christopher ; Kotagiri Ramamohanarao ; Bezdek James |
| 刊名 | Pattern Recognition
 |
| 出版日期 | 2011 |
| 卷号 | 44期号:2页码:222-235 |
| ISSN号 | 0031-3203 |
| 中文摘要 | Pairwise clustering methods have shown great promise for many real-world applications. However, the computational demands of these methods make them impractical for use with large data sets. The contribution of this paper is a simple but efficient method, called eSPEC, that makes clustering feasible for problems involving large data sets. Our solution adopts a "sampling, clustering plus extension" strategy. The methodology starts by selecting a small number of representative samples from the relational pairwise data using a selective sampling scheme; then the chosen samples are grouped using a pairwise clustering algorithm combined with local scaling; and finally, the label assignments of the remaining instances in the data are extended as a classification problem in a low-dimensional space, which is explicitly learned from the labeled samples using a cluster-preserving graph embedding technique. Extensive experimental results on several synthetic and real-world data sets demonstrate both the feasibility of approximately clustering large data sets and acceleration of clustering in loadable data sets of our method. © 2010 Elsevier Ltd. |
| 英文摘要 | Pairwise clustering methods have shown great promise for many real-world applications. However, the computational demands of these methods make them impractical for use with large data sets. The contribution of this paper is a simple but efficient method, called eSPEC, that makes clustering feasible for problems involving large data sets. Our solution adopts a "sampling, clustering plus extension" strategy. The methodology starts by selecting a small number of representative samples from the relational pairwise data using a selective sampling scheme; then the chosen samples are grouped using a pairwise clustering algorithm combined with local scaling; and finally, the label assignments of the remaining instances in the data are extended as a classification problem in a low-dimensional space, which is explicitly learned from the labeled samples using a cluster-preserving graph embedding technique. Extensive experimental results on several synthetic and real-world data sets demonstrate both the feasibility of approximately clustering large data sets and acceleration of clustering in loadable data sets of our method. © 2010 Elsevier Ltd. |
| 收录类别 | EI |
| 语种 | 英语 |
| 公开日期 | 2013-10-08 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16176]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Wang Liang,Leckie Christopher,Kotagiri Ramamohanarao,et al. approximate pairwise clustering for large data sets via sampling plus extension[J]. Pattern Recognition,2011,44(2):222-235. |
| APA | Wang Liang,Leckie Christopher,Kotagiri Ramamohanarao,&Bezdek James.(2011).approximate pairwise clustering for large data sets via sampling plus extension.Pattern Recognition,44(2),222-235. |
| MLA | Wang Liang,et al."approximate pairwise clustering for large data sets via sampling plus extension".Pattern Recognition 44.2(2011):222-235. |
入库方式： OAI收割
来源：软件研究所
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