Recent work on explainable clustering allows describing clusters when the
features are interpretable. However, much modern machine learning focuses on
complex data such as images, text, and graphs where deep learning is used but
the raw features of data are not interpretable. This paper explores a novel
setting for performing clustering on complex data while simultaneously
generating explanations using interpretable tags. We propose deep descriptive
clustering that performs sub-symbolic representation learning on complex data
while generating explanations based on symbolic data. We form good clusters by
maximizing the mutual information between empirical distribution on the inputs
and the induced clustering labels for clustering objectives. We generate
explanations by solving an integer linear programming that generates concise
and orthogonal descriptions for each cluster. Finally, we allow the explanation
to inform better clustering by proposing a novel pairwise loss with
self-generated constraints to maximize the clustering and explanation module's
consistency. Experimental results on public data demonstrate that our model
outperforms competitive baselines in clustering performance while offering
high-quality cluster-level explanations.

本文提出了一种新型的复杂数据聚类算法，利用符号数据进行言语解释，通过最大化模块之间的一致性产生更好的聚类效果和高质量的聚类级别解释，实验结果表明，该模型在聚类性能方面优于竞争对手，并提供高质量的聚类级别解释。