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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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Fast Flexible Bipartite Graph Model for Co-Clustering
影响因子：9.235
DOI码：10.1109/TKDE.2022.3194275
所属单位：西南交通大学
发表刊物：IEEE Transactions on Knowledge and Data Engineering
刊物所在地：UNITED STATES
关键字：Co-clustering, Bipartite graph partition, Faster performance, Flexibility;
摘要：Co-clustering methods make use of the correlation between samples and attributes to explore the co-occurrence structure in data. These methods have played a significant role in gene expression analysis, image segmentation, and document clustering. In bipartite graph partition-based co-clustering methods, the relationship between samples and attributes is described by constructing a diagonal symmetric bipartite graph matrix, which is clustered by the philosophy of spectral clustering. However, this not only has high time complexity but also the same number of row and column clusters. In fact, the number of categories of rows and columns often changes in the real world. To address these problems, this paper proposes a novel fast flexible bipartite graph model for the co-clustering method (FBGPC) that directly uses the original matrix to construct the bipartite graph. Then, it uses the inflation operation to partition the bipartite graph in order to learn the co-occurrence structure of the original data matrix based on the inherent relationship between bipartite graph partitioning and co-clustering. Finally, hierarchical clustering is used to obtain the clustering results according to the set relationship of the co-occurrence structure. Extensive empirical results show the effectiveness of our proposed model and verify the faster performance, generality, and flexibility of our model.
合写作者：Zhiguo Long,李天瑞
第一作者：Wei Chen
论文类型：学术论文
通讯作者：Hongjun Wang
学科门类：工学
一级学科：计算机科学与技术
页面范围：6930-6940
ISSN号：1041-4347
是否译文：否
发表时间：2023-07-27