# Predictive clustering

arXiv: https://arxiv.org/abs/1903.08125 · HTML: https://mlsanity.com/paper/1903.08125/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Skeleton Clustering: Dimension-Free Density-based Clustering](https://mlsanity.com/paper/2104.10770/) (2021)
- [Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams](https://mlsanity.com/paper/2601.04741/) (2026)
- [Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint](https://mlsanity.com/paper/2402.03388/) (2024)
- [Subjective Behaviors and Preferences in LLM: Language of Browsing](https://mlsanity.com/paper/2508.15474/) (2025)

## Abstract (arXiv)

We show how to convert any clustering into a prediction set. This has the effect of converting the clustering into a (possibly overlapping) union of spheres or ellipsoids. The tuning parameters can be chosen to minimize the size of the prediction set. When applied to k-means clustering, this method solves several problems: the method tells us how to choose k, how to merge clusters and how to replace the Voronoi partition with more natural shapes. We show that the same reasoning can be applied to other clustering methods.