# From Clustering to Cluster Explanations via Neural Networks

## TL;DR

This paper introduces a novel framework that explains cluster assignments in terms of input features by neuralizing clustering models, enabling efficient interpretation and assessment of clustering results.

## Contribution

It presents the first method to explain cluster assignments via neural networks, bridging clustering and explainable AI in an innovative way.

## Key findings

- Effective explanation of cluster assignments using neural networks
- Ability to assess cluster quality and extract insights
- Demonstrated on multiple data showcases

## Abstract

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, 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.