A Systematic Review and Categorization of Loss Functions in Deep Clustering

Abstract- Clustering techniques perform the task of discovering underlying patterns and structures in data. They play a crucial role in fields such as big data analytics, recommendation systems, and medical diagnostics, driving intelligent decision-making and efficient data processing. Deep clustering, with its strong ability to extract features, effectively overcomes the shortcomings of traditional clustering techniques, making it a prominent area of current research. Among these methods, the loss function, as the core component of deep clustering, guides the model in optimizing data representation, ensuring the effectiveness and stability of feature extraction from high-dimensional and complex data. However, existing studies primarily focus on the deep learning architecture, with few offering a systematic analysis from the perspective of loss functions. This paper reviews the current state of deep clustering research from the loss function viewpoint and categorizes relevant algorithms based on the characteristics of their loss functions. By analyzing the strengths and weaknesses of various loss functions, four essential elements for an effective loss function are proposed: information preservation, balance, robustness, and scalability. Future research directions are explored with respect to these four aspects.