From Clustering to Cluster Explanations via Neural Networks

A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly focused on supervised learning, in particular, deep neural network classifiers. In many practical problems, however, the label information is not given and the goal is instead to discover the underlying structure of the data, for example, its clusters. While powerful methods exist for extracting the cluster structure in data, they typically do not answer the question why a certain data point has been assigned to a given cluster. We propose a new framework that can, for the first time, explain cluster assignments in terms of input features in an efficient and reliable manner. It is based on the novel insight that clustering models can be rewritten as neural networks-or neuralized. Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features. Several showcases demonstrate the ability of our method to assess the quality of learned clusters and to extract novel insights from the analyzed data and representations.

Automated summary

In recent years, there has been a trend in machine learning to enhance learned models with the ability to explain their predictions. This field, known as explainable AI (XAI), has mainly focused on supervised learning, particularly deep neural network classifiers. However, in many practical problems where label information is not given, the goal is to discover the underlying structure of the data, such as its clusters. This study proposes a novel framework that can explain cluster assignments in terms of input features efficiently and reliably, by rewriting clustering models as neural networks.