research-article

Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering

Authors:

Xuejun LiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 5

Article No.: 62, Pages 1 - 35

https://doi.org/10.1145/3564701

Published: 27 February 2023 Publication History

Abstract

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.

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Index Terms

Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 5

June 2023

386 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3583066

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 27 February 2023

Online AM: 27 September 2022

Accepted: 19 September 2022

Revised: 31 August 2022

Received: 28 January 2022

Published in TKDD Volume 17, Issue 5

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https://doi.org/10.1109/TPAMI.2024.3507857
Guo YBo DYang CLu ZZhang ZLiu JPeng YShi C(2025)Data-Centric Graph Learning: A SurveyIEEE Transactions on Big Data10.1109/TBDATA.2024.348941211:1(1-20)Online publication date: Feb-2025
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Zhou PDu LLiu XLing ZJi XLi XShen Y(2024)Partial Clustering EnsembleIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332191336:5(2096-2109)Online publication date: May-2024
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