.png){kind=icon}
返回
A clustering method based on boosting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2003.12.018.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g. k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality. (C) 2004 Elsevier B.V. All rights reserved.
Keyword:
ensemble clustering
unsupervised learning
partitions schemes
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
暂无机构信息
.png){kind=icon}
