Supervised Clustering: How to Use SHAP Values for Better Cluster Analysis

Cluster analysis is a popular method for identifying subgroups within a population, but the results are often challenging to interpret and action. Supervised clustering leverages SHAP values to identify better-separated clusters using a more structured representation of the data. This article demonstrates the benefits of supervised clustering with an example based on simulated data. I provide a simple illustration of a methodology I applied to COVID-19 symptom clustering for a paper at the ECML PKDD 2021 conference. This article assumes a basic understanding of SHAP, which is a technique for deconstructing a machine learning model's predictions into a sum of contributions from each of its input variables.