Clustering is a fundamental learning task widely used as a first step in data
analysis. For example, biologists often 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 that
predicts interpretable cluster assignments at the instance and cluster levels.
First, we present a self-supervised procedure to identify a subset of
informative features from each data point. Then, we design a model that
predicts cluster assignments and a gate matrix that leads to cluster-level
feature selection. We show that the proposed method can reliably predict
cluster assignments using synthetic and real data. Furthermore, we verify that
our model leads to interpretable results at a sample and cluster level.

本文提出了一种新的深度学习框架，可以在 instance 和 cluster 级别预测可解释的聚类分配，并在基因组序列、医疗记录或图像等数据上验证了该方法的可靠性和可解释性。

可解释的深度聚类

Interpretable Deep Clustering

Antimicrobial resistance is an important public health concern that has
implications in the practice of medicine worldwide. Accurately predicting
resistance phenotypes from genome sequences shows great promise in promoting
better use of antimicrobial agents, by determining which antibiotics are likely
to be effective in specific clinical cases. In healthcare, this would allow for
the design of treatment plans tailored for specific individuals, likely
resulting in better clinical outcomes for patients with bacterial infections.
In this work, we present the recent work of Drouin et al. (2016) on using Set
Covering Machines to learn highly interpretable models of antibiotic resistance
and complement it by providing a large scale application of their method to the
entire PATRIC database. We report prediction results for 36 new datasets and
present the Kover AMR platform, a new web-based tool allowing the visualization
and interpretation of the generated models.

本研究基于 Drouin 等人 (2016) 的相关工作，运用集合覆盖机对抗菌素抗性模型进行高度解释性学习，以及大规模的应用到整个 PATRIC 数据库中，并提出了新的 Kover AMR 平台，用于可视化和解释生成的模型，以期准确预测抗生素表现，根据不同的临床情况，为每个人的治疗计划提供定制服务，从而提高临床结果。