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Related Experiment Video

Updated: Oct 11, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering.

Adil M Bagirov, Sona Taheri, Fusheng Bai

    IEEE Transactions on Neural Networks and Learning Systems
    |December 1, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an adaptive semisupervised clustering (A-SSC) algorithm for improved data clustering. The A-SSC algorithm effectively identifies compact, well-separated clusters using pairwise constraints and a novel optimization approach.

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    Area of Science:

    • Machine Learning
    • Optimization Theory
    • Data Mining

    Background:

    • Semisupervised clustering (SSC) integrates labeled and unlabeled data for enhanced pattern recognition.
    • Existing SSC methods often struggle with nonconvex and nonsmooth objective functions, limiting their performance.
    • Pairwise constraints (must-link and cannot-link) provide valuable prior information for guiding clustering.

    Purpose of the Study:

    • To develop a novel semisupervised clustering model that effectively handles nonconvex and nonsmooth objective functions.
    • To introduce an adaptive semisupervised clustering (A-SSC) algorithm for solving the proposed model.
    • To evaluate the performance of the A-SSC algorithm against existing state-of-the-art methods.

    Main Methods:

    • A nonconvex nonsmooth optimization model for semisupervised clustering (SSC) incorporating pairwise constraints (must-link and cannot-link).
    • An adaptive SSC (A-SSC) algorithm combining nonsmooth optimization with an incremental approach and auxiliary SSC problems.
    • The discrete gradient method (DGM) for solving the underlying nonsmooth optimization problems without subgradient evaluations.

    Main Results:

    • The A-SSC algorithm demonstrated superior performance compared to four benchmarking SSC algorithms on diverse datasets.
    • The proposed algorithm excels in producing compact and well-separated clusters.
    • A-SSC effectively satisfies a significant portion of the imposed pairwise constraints.

    Conclusions:

    • The A-SSC algorithm offers a robust and efficient solution for semisupervised clustering with pairwise constraints.
    • The discrete gradient method provides an effective means to optimize nonconvex nonsmooth functions in clustering.
    • The A-SSC algorithm represents a significant advancement in semisupervised clustering, particularly for complex datasets with constraints.