硕士生导师
个人信息Personal Information
学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Semi-supervised Hierarchical Clustering Ensemble and Its Application
影响因子：5.779
DOI码：10.1016/j.neucom.2015.09.009
所属单位：西南交通大学
发表刊物：Neurocomputing
刊物所在地：NETHERLANDS
关键字：Clustering ensembleSemi-supervisedCHAMELEONFault diagnosis
摘要：Clustering ensemble is an important part of ensemble learning. It aims to study and integrate multiple clustering results from different clustering algorithms or same algorithm with different initial parameters for the same dataset. CHAMELEON is a hierarchical clustering algorithm which can discover natural clusters of different shapes and sizes as the result of its merging decision dynamically adapts to the different clustering model characterized. Inspired by the idea of CHAMELEON, the paper proposes a novel clustering ensemble models including semi-supervised method and discusses its application in fault diagnosis of high speed train (HST) running gear. The contributions of this paper include: constructing a sparse graph via the similarity matrix which aggregates multiple clustering results; partitioning the sparse graph (vertex=object, edge weight=similarity) into a large number of relatively small sub-clusters; obtaining the final clustering partition by merging these sub-clusters repeatedly. The experimental results demonstrate that our method outperforms some of state-of-the-art ensemble algorithms regarding the accuracy and stability and recognizes fault patterns of HST running gear effectively.
合写作者：Hongjun Wang,李天瑞, Huanlai Xin
第一作者：Wenchao Xiao
论文类型：学术论文
通讯作者：Yan Yang
论文编号：20155101706995
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 173, Part 3
期号：15 January 2016
页面范围：Pages 1362-1376
ISSN号：0925-2312
是否译文：否
发表时间：2015-09-10