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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Bayesian Clustering Ensemble
DOI码：10.1002/sam.10098
所属单位：西南交通大学
发表刊物：Statistical Analysis and Data Mining
关键字：cluster ensembles Bayesian models
摘要：Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, row- or column-distributed cluster ensembles. Existing cluster ensemble algorithms are applicable only to a small subset of these variants. In this paper, we propose Bayesian cluster ensemble (BCE), which is a mixed-membership model for learning cluster ensembles, and is applicable to all the primary variants of the problem. We propose a variational approximation based algorithm for learning Bayesian cluster ensembles. BCE is further generalized to deal with the case where the features of original data points are available, referred to as generalized BCE (GBCE). We compare BCE extensively with several other cluster ensemble algorithms, and demonstrate that BCE is not only versatile in terms of its applicability but also outperforms other algorithms in terms of stability and accuracy. Moreover, GBCE can have higher accuracy than BCE, especially with only a small number of available base clusterings.
合写作者：Arindam Banerjee.
第一作者：Hongjun Wang
论文类型：学术论文
通讯作者：Hanhuai Shan
学科门类：工学
一级学科：计算机科学与技术
期号：4
页面范围：54–70 2011
是否译文：否
发表时间：2011-02-09