影响因子：14.255
DOI码：10.1109/TNNLS.2019.2919723
发表刊物：IEEE Transactions on Neural Networks and Learning Systems
关键字：Bayesian adaptive resonance theory (BART); data snapshot; imbalanced data; incremental clustering algorithm; local distribution
摘要：Most of the existing Bayesian clustering algorithms perform well on the balanced data. When the data are highly imbalanced, these Bayesian clustering algorithms tend to strongly favor the larger clusters, but provide a notably low detection of the smaller clusters. In this paper, we present an incremental local distribution-based clustering algorithm with the Bayesian adaptive resonance theory (ILBART). This algorithm is developed to adapt itself to a changing environment without using any predefined parameters. The algorithm not only accurately finds the clusters, even in data sets with a severely imbalanced distribution, but also efficiently processes the dynamic data according to the evolving relationships among the clusters. We test our proposed algorithm with experiments conducted on several imbalanced data sets. The experimental results show that our proposed algorithm performs well for clustering imbalanced data and can also obtain a better performance than many other relevant clustering algorithms in several performance indices.
论文类型：期刊论文
论文编号：8746820
学科门类：工学
文献类型：J
卷号：30
期号：11
页面范围：3496 - 3504
ISSN号：2162237X
是否译文：否
发表时间：2019-01-01
收录刊物：SCI
发布期刊链接：https://ieeexplore.ieee.org/abstract/document/8746820?casa_token=PWt8SetguWMAAAAA:dEG33AaehH_NH_sE-gSJ6Hi5CvNMwaqHLkR-LndTvWcGEG0DhNqFNpWmlWATUa13Ti19cwkbVNeyHQ