We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform well for both non-complex topology and complex topology datasets. To address the problem, we propose a constraint utilizing symmetric InfoNCE, which helps an objective of deep clustering method in the scenario train the model so as to be efficient for not only non-complex topology but also complex topology datasets. Additionally, we provide several theoretical explanations of the reason why the constraint can enhances performance of deep clustering methods. To confirm the effectiveness of the proposed constraint, we introduce a deep clustering method named MIST, which is a combination of an existing deep clustering method and our constraint. Our numerical experiments via MIST demonstrate that the constraint is effective. In addition, MIST outperforms other state-of-the-art deep clustering methods for most of the commonly used ten benchmark datasets.

翻译：我们考虑深度聚类的场景，其中可用先验知识有限。在此场景下，现有少数最先进的深度聚类方法对于非复杂拓扑和复杂拓扑数据集均难以取得优异性能。为解决该问题，我们提出利用对称InfoNCE引入约束，该约束有助于深度聚类方法在该场景下的目标函数训练模型，使其不仅对非复杂拓扑数据集有效，对复杂拓扑数据集也同样高效。此外，我们从理论上阐释了该约束提升深度聚类方法性能的原因。为验证所提约束的有效性，我们提出名为MIST的深度聚类方法，该方法融合现有深度聚类方法与我们的约束。通过MIST进行的数值实验表明该约束确实有效。同时，在十个常用基准数据集的大多数上，MIST的表现优于其他最先进的深度聚类方法。