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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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Markov clustering ensemble
DOI码：10.1016/j.knosys.2022.109196
发表刊物：Knowledge-Based Systems
刊物所在地：NETHERLANDS
关键字：Machine learning; Unsupervised learning; Markov clustering ensemble model; Markov clustering ensemble algorithm
摘要：Clustering ensemble is an unsupervised ensemble learning method that is very important in machine learning, since it integrates multiple weak base clustering results to produce a strong consistency result. This paper proposes the Markov clustering ensemble (MCE) model to solve the weak stability and robustness of soft clustering ensemble. First, the base clustering algorithms are regarded as new features of the original datasets. Then, the results of the base clustering algorithms are the values of these features, which can break through the framework of consensus cluster ensemble. Second, as the base clustering results are discrete data, the maximum information coefficient is applied to measure their similarity. Accordingly, a graph-based cluster ensemble model can be constructed with row vectors as vertices and the similarity between row vectors as edges. Then, the Markov process can be applied to infer the graph-based cluster ensemble model; therefore, the MCE algorithm is designed according to the inference. To test the performance of the MCE algorithm, clustering algorithms and clustering ensemble algorithms are used to conduct comparative experiments on ten datasets. The experimental results show that the MCE algorithm outperforms the other algorithms in terms of accuracy and purity.
合写作者：Tianrui Li
第一作者：Luqing Wang
论文类型：SCI
通讯作者：Junwei Luo,Hongjun Wang
文献类型：J
期号：251
页面范围：109196
ISSN号：0950-7051
是否译文：否
发表时间：2022-06-04
收录刊物：SCI