Distribution learning finds probability density functions from a set of data samples, whereas clustering aims to group similar data points to form clusters. Although there are deep clustering methods that employ distribution learning methods, past work still lacks theoretical analysis regarding the relationship between clustering and distribution learning. Thus, in this work, we provide a theoretical analysis to guide the optimization of clustering via distribution learning. To achieve better results, we embed deep clustering guided by a theoretical analysis. Furthermore, the distribution learning method cannot always be directly applied to data. To overcome this issue, we introduce a clustering-oriented distribution learning method called Monte-Carlo Marginalization for Clustering. We integrate Monte-Carlo Marginalization for Clustering into Deep Clustering, resulting in Deep Clustering via Distribution Learning (DCDL). Eventually, the proposed DCDL achieves promising results compared to state-of-the-art methods on popular datasets. Considering a clustering task, the new distribution learning method outperforms previous methods as well.

本研究解决了深度聚类与分布学习之间理论分析缺乏的问题。提出了一种名为Monte-Carlo Marginalization for Clustering的新方法，增强了深度聚类的优化指导，使得该方法在流行数据集上的表现优于现有的最先进方法。这表明新的分布学习方法在聚类任务中具有更强的效果。

通过分布学习进行深度聚类