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Related Experiment Video

Updated: Apr 26, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Collaborative fuzzy clustering from multiple weighted views.

Yizhang Jiang, Fu-Lai Chung, Shitong Wang

    IEEE Transactions on Cybernetics
    |July 29, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new multiview fuzzy clustering algorithm, weighted view collaborative fuzzy c-means (WV-FCM), to effectively combine clustering results and identify view importance. Experiments show WV-FCM outperforms existing methods.

    Related Experiment Videos

    Last Updated: Apr 26, 2026

    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
    06:01

    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

    Published on: December 12, 2019

    10.1K

    Area of Science:

    • Machine Learning
    • Data Mining
    • Pattern Recognition

    Background:

    • Multiview clustering is crucial for integrating information from diverse data sources.
    • Key challenges include effectively combining results from individual views and determining their relative importance.

    Purpose of the Study:

    • To propose a novel multiview fuzzy clustering algorithm that addresses the combination and importance identification issues.
    • To introduce the collaborative fuzzy c-means (Co-FCM) and its weighted view extension (WV-Co-FCM).

    Main Methods:

    • Developed a new objective function with two penalty terms for fuzzy clustering.
    • Introduced the weighted view collaborative fuzzy c-means (WV-Co-FCM) algorithm to assign importance weights to different data views.
    • Analyzed the relationship between WV-Co-FCM and Collaborative Fuzzy K-Means (Co-FKM).

    Main Results:

    • The proposed WV-Co-FCM algorithm effectively tackles both view combination and importance identification.
    • Experimental results demonstrate WV-Co-FCM's superior or comparable performance against state-of-the-art algorithms on various multiview datasets.
    • The algorithm successfully identifies the importance of different data views.

    Conclusions:

    • WV-Co-FCM offers an effective approach for multiview fuzzy clustering.
    • The method provides a robust framework for handling complex multiview data by weighting view importance.
    • This research advances the field of multiview clustering with a novel and effective algorithm.