计算机工程与应用2018，Vol.54Issue(3)：150-159,183,11.DOI:10.3778/j.issn.1002-8331.1608-0411
基于特征关系的加权投票聚类集成研究
Clustering ensemble with weighted voting based on feature correlation
江志良 1侯远 1吴敏1
作者信息
- 1. 华东师范大学 计算机科学与软件工程学院,上海 200062
- 折叠
摘要
Abstract
To process complicated data that possesses many features, clustering ensemble based on weighted-voting is able to make a trade-off between clustering members with different qualities and improves the accuracy and stability. Towards subset selection and weight calculation, a sub-feature selection based on minimal correlation is proposed and in terms with feature-correlation, 5 different weight-calculation methods for clustering member are analyzed and compared. The experimental results show that subset generation based on minimal correlation is more effective than random sampling, and clustering ensemble based on any of the 5 weight-calculation methods gain higher accuracy than single clustering. Time-consumption among these 5 methods differ greatly.关键词
聚类集成/特征选择/加权融合Key words
clustering ensemble/feature selection/weighted voting分类
信息技术与安全科学引用本文复制引用
江志良,侯远,吴敏..基于特征关系的加权投票聚类集成研究[J].计算机工程与应用,2018,54(3):150-159,183,11.
