research-article

Fast Flexible Bipartite Graph Model for Co-Clustering

Authors:

Tianrui LiAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 35, Issue 7

Pages 6930 - 6940

https://doi.org/10.1109/TKDE.2022.3194275

Published: 01 July 2023 Publication History

Abstract

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.

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Cited By

Battaglia EPeiretti FPensa R(2024)Co-clustering: A Survey of the Main Methods, Recent Trends, and Open ProblemsACM Computing Surveys10.1145/369887557:2(1-33)Online publication date: 4-Oct-2024
https://dl.acm.org/doi/10.1145/3698875
Wang HSong YChen WLuo ZLi CLi T(2024)A Survey of Co-ClusteringACM Transactions on Knowledge Discovery from Data10.1145/368179318:9(1-28)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3681793
Wang DLi TDeng PLuo ZZhang PLiu KHuang W(2024)DNSRF: Deep Network-based Semi-NMF Representation FrameworkACM Transactions on Intelligent Systems and Technology10.1145/367040815:5(1-20)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670408
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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 35, Issue 7

July 2023

1090 pages

ISSN:1041-4347

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1041-4347 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 July 2023

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Cited By

Battaglia EPeiretti FPensa R(2024)Co-clustering: A Survey of the Main Methods, Recent Trends, and Open ProblemsACM Computing Surveys10.1145/369887557:2(1-33)Online publication date: 4-Oct-2024
https://dl.acm.org/doi/10.1145/3698875
Wang HSong YChen WLuo ZLi CLi T(2024)A Survey of Co-ClusteringACM Transactions on Knowledge Discovery from Data10.1145/368179318:9(1-28)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3681793
Wang DLi TDeng PLuo ZZhang PLiu KHuang W(2024)DNSRF: Deep Network-based Semi-NMF Representation FrameworkACM Transactions on Intelligent Systems and Technology10.1145/367040815:5(1-20)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670408
Chen WWang HZhang YDeng PLuo ZLi T(2024)T-Distributed Stochastic Neighbor Embedding for Co-Representation LearningACM Transactions on Intelligent Systems and Technology10.1145/362782315:2(1-18)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3627823
Battaglia EPeiretti FPensa R(2024)Fast parameterless prototype-based co-clusteringMachine Language10.1007/s10994-023-06474-y113:4(2153-2181)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10994-023-06474-y
Gao SZhang HChen XTao CZhao DYan R(2023)A Trend of AI Conference Convergence in Similarity: An Empirical Study Through Trans-Temporal Heterogeneous GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324360235:9(9642-9655)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3243602

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