硕士生导师
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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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Spectral co-clustering ensemble
影响因子：8.139
DOI码：10.1016/j.knosys.2015.03.027
所属单位：西南交通大学
发表刊物：KNOWLEDGE-BASED SYSTEMS
刊物所在地：NETHERLANDS
关键字：Co-clusteringEnsemble learningSpectral co-clustering ensembleSpectral algorithmMutual information
摘要：The goal of co-clustering is to simultaneously cluster the rows and columns of an input data matrix. It overcomes several limitations associated with traditional clustering methods by allowing automatic discovery of similarity based on a subset of attributes. However, different co-clustering models usually produce very distinct results since each algorithm has its own bias due to the optimization of different criteria. The idea of combining different co-clustering results emerged as an alternative approach for improving the performance of co-clustering algorithms. Similar to clustering ensembles, co-clustering ensembles provide a framework for combining multiple base co-clusterings of a dataset to generate a stable and robust consensus co-clustering result. In this paper, a novel co-clustering ensemble algorithm named spectral co-clustering ensemble (SCCE) is presented. SCCE performs ensemble tasks on base row clusters and column clusters of a dataset simultaneously, and obtains an optimization co-clustering result. Meanwhile, SCCE is a matrix decomposition based approach which can be formulated as a bipartite graph partition problem and solve it efficiently with the selected eigenvectors. To the best of our knowledge, this is the first work on using spectral algorithm for co-clustering ensemble. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method. Our study also shows that SCCE has some favorable merits compared with many state of the art methods.
合写作者：李天瑞, Yan Yang
第一作者：Shudong Huang
论文类型：学术论文
通讯作者：Hongjun Wang
论文编号：20151600754270
学科门类：工学
一级学科：计算机科学与技术
卷号：Volume 84
期号：August 2015
页面范围：Pages 46-55
ISSN号：0950-7051
是否译文：否
发表时间：2015-08-03