In recent years, with the great success of deep learning and especially deep unsupervised learning, many deep architectural clustering methods, collectively known as deep clustering, have emerged. Deep clustering shows the potential to outperform traditional methods, especially in handling complex high-dimensional data, taking full advantage of deep learning. To achieve a comprehensive overview of the field of deep clustering, this review systematically explores deep clustering methods and their various applications. First, the basic network architecture of deep clustering is described in detail, including the common network frameworks, and loss functions. Subsequently, deep clustering is divided into several categories based on the network architecture, and benchmark datasets and evaluation metrics in the field are introduced. Next, the real-world applications of deep clustering are explored in depth, providing successful cases in the fields of bioinformatics, medicine, anomaly detection, and image processing, highlighting the broad applicability of deep clustering in solving real-world challenges. Finally, the paper summarizes its contributions and explores potential directions for future research in deep clustering.

中文翻译：

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深度聚类概述


近年来，随着深度学习尤其是深度无监督学习的巨大成功，出现了许多深度架构的聚类方法，统称为深度聚类。深度聚类显示出超越传统方法的潜力，特别是在充分利用深度学习处理复杂的高维数据方面。为了全面概述深度聚类领域，本文系统地探讨了深度聚类方法及其各种应用。首先，详细描述了深度聚类的基本网络架构，包括常见的网络框架和损失函数。随后，根据网络架构将深度聚类分为几类，并介绍了该领域的基准数据集和评估指标。接下来，深入探讨深度聚类的实际应用，提供生物信息学、医学、异常检测和图像处理等领域的成功案例，凸显深度聚类在解决现实世界挑战方面的广泛适用性。最后，本文总结了其贡献并探讨了深度聚类未来研究的潜在方向。