Predictive K-means with local models

TL;DR

This paper introduces two novel algorithms that enhance clustering for predictive tasks by transforming data representations based on class densities, achieving competitive prediction accuracy while maintaining interpretability.

Contribution

It proposes a new method for predictive clustering using representation changes guided by class densities, improving interpretability and prediction performance.

Findings

Algorithms are competitive with supervised classifiers in prediction accuracy.

The methods offer interpretable clusters aligned with class labels.

Effective across various datasets.

Abstract

Supervised classification can be effective for prediction but sometimes weak on interpretability or explainability (XAI). Clustering, on the other hand, tends to isolate categories or profiles that can be meaningful but there is no guarantee that they are useful for labels prediction. Predictive clustering seeks to obtain the best of the two worlds. Starting from labeled data, it looks for clusters that are as pure as possible with regards to the class labels. One technique consists in tweaking a clustering algorithm so that data points sharing the same label tend to aggregate together. With distance-based algorithms, such as k-means, a solution is to modify the distance used by the algorithm so that it incorporates information about the labels of the data points. In this paper, we propose another method which relies on a change of representation guided by class densities and then carries out clustering in this new representation space. We present two new algorithms using this technique and show on a variety of data sets that they are competitive for prediction performance with pure supervised classifiers while offering interpretability of the clusters discovered.