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From Concrete to Abstract: Multi-View Clustering on Relational Knowledge.

Ke Liang, Lingyuan Meng, Hao Li

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    This study introduces relational knowledge multi-view clustering (RK-MVC) for abstract concepts. RK-MVC enhances clustering by incorporating sample-global correlations, improving performance on relational knowledge datasets.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Mining

    Background:

    • Multi-view clustering (MVC) primarily focuses on concrete objects, neglecting abstract concepts like knowledge.
    • Abstract concepts, particularly relational knowledge, are crucial for cognition and daily life.
    • Existing MVC methods lack effective strategies for clustering abstract relational data.

    Purpose of the Study:

    • To address the gap in multi-view clustering for abstract relational knowledge.
    • To establish research foundations by constructing new datasets for relational knowledge MVC.
    • To propose a novel framework, Relational Knowledge Multi-View Clustering (RK-MVC), for abstract concept clustering.

    Main Methods:

    • Developed a relational knowledge multi-view clustering (RK-MVC) paradigm.
    • Constructed 8 novel datasets specifically for relational knowledge clustering tasks.
    • Proposed a method to compensate for omitted sample-global correlations using structural knowledge.
    • Implemented coarse-grained and fine-grained approaches to generate sample-global correlations.
    • Integrated basic consensus features with learned correlation features via weighted fusion.

    Main Results:

    • Demonstrated the effectiveness of the proposed RK-MVC paradigm.
    • Validated the approach across 9 typical multi-view clustering backbones.
    • Showcased performance improvements from 7 different aspects compared to baseline methods.
    • Highlighted the adaptability of the sample-global correlation learning module to various MVC backbones.

    Conclusions:

    • Relational knowledge multi-view clustering (RK-MVC) is a viable and important research direction.
    • The proposed RK-MVC framework effectively clusters abstract relational knowledge.
    • The method shows promising capacity and adaptability, laying groundwork for future research in abstract concept clustering.