Clustering is a fundamental learning task widely used as a first step in data analysis. For example, biologists use cluster assignments to analyze genome sequences, medical records, or images. Since downstream analysis is typically performed at the cluster level, practitioners seek reliable and interpretable clustering models. We propose a new deep-learning framework for general domain tabular data that predicts interpretable cluster assignments at the instance and cluster levels. First, we present a self-supervised procedure to identify the subset of the most informative features from each data point. Then, we design a model that predicts cluster assignments and a gate matrix that provides cluster-level feature selection. Overall, our model provides cluster assignments with an indication of the driving feature for each sample and each cluster. We show that the proposed method can reliably predict cluster assignments in biological, text, image, and physics tabular datasets. Furthermore, using previously proposed metrics, we verify that our model leads to interpretable results at a sample and cluster level. Our code is available at https://github.com/jsvir/idc.

翻译：聚类是一种基础学习任务，在数据分析中被广泛用作首要步骤。例如，生物学家利用聚类分配来分析基因组序列、医疗记录或图像。由于下游分析通常在聚类层面进行，实践者需要可靠且可解释的聚类模型。我们提出了一种面向通用领域表格数据的新型深度学习框架，该框架可在实例层面和聚类层面预测可解释的聚类分配。首先，我们提出一种自监督方法，用于从每个数据点中识别最具信息量的特征子集。随后，我们设计了一个预测聚类分配的模型，以及一个提供聚类层面特征选择的门控矩阵。总体而言，我们的模型提供的聚类分配能够同时指示每个样本和每个聚类的驱动特征。实验表明，所提方法能够在生物、文本、图像及物理领域的表格数据集中可靠地预测聚类分配。此外，通过使用先前提出的评估指标，我们验证了该模型能够在样本层面和聚类层面产生可解释的结果。我们的代码公开于 https://github.com/jsvir/idc。