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research-article

A Survey of Co-Clustering

Authors:

Tianrui LiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 9

Article No.: 224, Pages 1 - 28

https://doi.org/10.1145/3681793

Published: 20 November 2024 Publication History

Abstract

Co-clustering is to cluster samples and features simultaneously, which can also reveal the relationship between row clusters and column clusters. Therefore, lots of scientists have drawn much attention to conduct extensive research on it, and co-clustering is widely used in recommendation systems, gene analysis, medical data analysis, natural language processing, image analysis, and social network analysis. In this article, we survey the entire research aspect of co-clustering, especially the latest advances in co-clustering, and discover the current research challenges and future directions. First, due to different views from researchers on the definition of co-clustering, this article summarizes the definition of co-clustering and its extended definitions, as well as related issues, based on the perspectives of various scientists. Second, existing co-clustering techniques are approximately categorized into four classes: information-theory-based, graph-theory-based, matrix-factorization-based, and other theories-based. Third, co-clustering is applied in various aspects such as recommendation systems, medical data analysis, natural language processing, image analysis, and social network analysis. Furthermore, 10 popular co-clustering algorithms are empirically studied on 10 benchmark datasets with 4 metrics—accuracy, purity, block discriminant index, and running time, and their results are objectively reported. Finally, future work is provided to get insights into the research challenges of co-clustering.

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Cited By

Wu ZLiu YShi XZhao XWang YZhang G(2024)GTGNN: Global Graph and Taxonomy Tree for Graph Neural Network Session-Based RecommendationWeb Information Systems and Applications10.1007/978-981-97-7707-5_3(29-40)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7707-5_3
Xu PNing ZXiao MFeng GLi XZhou YWang P(2024)scCDCG: Efficient Deep Structural Clustering for Single-Cell RNA-Seq via Deep Cut-Informed Graph EmbeddingDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_11(172-187)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5575-2_11

Index Terms

A Survey of Co-Clustering
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 9

November 2024

730 pages

EISSN:1556-472X

DOI:10.1145/3613722

Editor:
Jian Pei
Duke University, USA

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Publication History

Published: 20 November 2024

Online AM: 25 July 2024

Accepted: 20 July 2024

Revised: 19 May 2024

Received: 16 October 2023

Published in TKDD Volume 18, Issue 9

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Cited By

Wu ZLiu YShi XZhao XWang YZhang G(2024)GTGNN: Global Graph and Taxonomy Tree for Graph Neural Network Session-Based RecommendationWeb Information Systems and Applications10.1007/978-981-97-7707-5_3(29-40)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7707-5_3
Xu PNing ZXiao MFeng GLi XZhou YWang P(2024)scCDCG: Efficient Deep Structural Clustering for Single-Cell RNA-Seq via Deep Cut-Informed Graph EmbeddingDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_11(172-187)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5575-2_11

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