TDCC: A Trustworthy Deep Credal Clustering Method for Uncertain Data

Deep clustering has achieved remarkable success in handling various types of real-world data, but often suffers from overconfidence, forcing ambiguous samples into specific clusters even when the evidence is insufficient. To address this limitation, we propose trustworthy deep credal clustering, a novel framework for uncertainty that integrates deep neural networks with the Dempster-Shafer Theory of evidence (DST). This method leverages credal cluster structures to enhance the model's robustness against uncertain data. Our model can refrain from assigning uncertain samples to a specific cluster, thereby reducing errors and enhancing the model's trustworthiness. Theoretically, we derive closed-form solutions for updating cluster memberships and prototypes, employing a coordinate descent strategy to rigorously optimize the objective function. Experiments on various datasets confirm that our proposed trustworthy clustering method leads to enhanced overall clustering effectiveness. Code is available at https://github.com/H1nkik/Trustworthy-Clustering.