date:: 2022
issn:: 15662535
doi:: 10.1016/j.inffus.2021.09.003
title:: @Clustering ensemble via structured hypergraph learning
pages:: 171-179
volume:: 78
item-type:: journalArticle
original-title:: Clustering ensemble via structured hypergraph learning
language:: en
publication-title:: Information Fusion
journal-abbreviation:: Information Fusion
authors:: Peng Zhou, Xia Wang, Liang Du, Xuejun Li
library-catalog:: Engineering Village
links:: Local library, Web library

• Abstract
  • Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods. 2021 Elsevier B.V.
• Attachments
  • Zhou 等 - 2022 - Clustering ensemble via structured hypergraph lear.pdf {{zotero-imported-file AB8KFEEC, "Zhou 等 - 2022 - Clustering ensemble via structured hypergraph lear.pdf"}}
• Notes

  • 💡 Meta Data

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    Title
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    Clustering ensemble via structured hypergraph learning
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    |

    |
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    Journal
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    Information Fusion
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    |

    |
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    Authors
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    Peng Zhou; Xia Wang; Liang Du; Xuejun Li
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    |
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    Pub. date
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    2022
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    DOI
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    10.1016/j.inffus.2021.09.003
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    📜 研究背景 & 目的 & 意义 & 基础

    ---

    背景：

    目的：

    意义：

    基础：

    📊 研究内容

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    🚩 研究结论

    ---

    📌 创新 & 疑问

    ---

    创新：

    疑问：

    🔬 研究展望

• date:: 2022
  issn:: 15662535
  doi:: 10.1016/j.inffus.2021.09.003
  title:: @Clustering ensemble via structured hypergraph learning
  pages:: 171-179
  volume:: 78
  item-type:: journalArticle
  original-title:: Clustering ensemble via structured hypergraph learning
  language:: en
  publication-title:: Information Fusion
  journal-abbreviation:: Information Fusion
  authors:: Peng Zhou, Xia Wang, Liang Du, Xuejun Li
  library-catalog:: Engineering Village
  links:: Local library, Web library
• Abstract
  • Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods. 2021 Elsevier B.V.
• Attachments
  • Zhou 等 - 2022 - Clustering ensemble via structured hypergraph lear.pdf {{zotero-imported-file AB8KFEEC, "Zhou 等 - 2022 - Clustering ensemble via structured hypergraph lear.pdf"}}
• Notes

  • Clustering ensemble via structured hypergraph learning

    💡 Meta Data

    |
    |
    Title
    |
    |
    Clustering ensemble via structured hypergraph learning
    |
    |

    |
    |
    Journal
    |
    |
    Information Fusion
    |
    |

    |
    |
    Authors
    |
    |
    Peng Zhou; Xia Wang; Liang Du; Xuejun Li
    |
    |

    |
    |
    Pub. date
    |
    |
    2022
    |
    |

    |
    |
    DOI
    |
    |
    10.1016/j.inffus.2021.09.003
    |
    |

    📜 研究背景 & 目的 & 意义 & 基础

    ---

    背景：

    目的：

    意义：

    基础：

    📊 研究内容

    ---

    🚩 研究结论

    ---

    📌 创新 & 疑问

    ---

    创新：

    疑问：

    🔬 研究展望

• date:: 2022
  issn:: 15662535
  doi:: 10.1016/j.inffus.2021.09.003
  title:: @Clustering ensemble via structured hypergraph learning
  pages:: 171-179
  volume:: 78
  item-type:: journalArticle
  original-title:: Clustering ensemble via structured hypergraph learning
  language:: en
  publication-title:: Information Fusion
  journal-abbreviation:: Information Fusion
  authors:: Peng Zhou, Xia Wang, Liang Du, Xuejun Li
  library-catalog:: Engineering Village
  links:: Local library, Web library
• Abstract
  • Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods. 2021 Elsevier B.V.
• Attachments
  • Zhou 等 - 2022 - Clustering ensemble via structured hypergraph lear.pdf {{zotero-imported-file AB8KFEEC, "Zhou 等 - 2022 - Clustering ensemble via structured hypergraph lear.pdf"}}
• Notes

  • Clustering ensemble via structured hypergraph learning

    💡 Meta Data

    |
    |
    Title
    |
    |
    Clustering ensemble via structured hypergraph learning
    |
    |

    |
    |
    Journal
    |
    |
    Information Fusion
    |
    |

    |
    |
    Authors
    |
    |
    Peng Zhou; Xia Wang; Liang Du; Xuejun Li
    |
    |

    |
    |
    Pub. date
    |
    |
    2022
    |
    |

    |
    |
    DOI
    |
    |
    10.1016/j.inffus.2021.09.003
    |
    |

    📜 研究背景 & 目的 & 意义 & 基础

    ---

    背景：

    目的：

    意义：

    基础：

    📊 研究内容

    ---

    🚩 研究结论

    ---

    📌 创新 & 疑问

    ---

    创新：

    疑问：

    🔬 研究展望