# A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates

arXiv: https://arxiv.org/abs/2204.08205 · HTML: https://mlsanity.com/paper/2204.08205/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 53 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation](https://mlsanity.com/paper/1910.10583/) (2019)
- [Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization](https://mlsanity.com/paper/1910.07817/) (2019)

## Built on by (most cited first, 1 of 1)

- [An integrated perspective of robustness in regression through the lens of the bias-variance trade-off](https://mlsanity.com/paper/2407.10418/) (2024)

## Abstract (arXiv)

In this study, we examine a clustering problem in which the covariates of each individual element in a dataset are associated with an uncertainty specific to that element. More specifically, we consider a clustering approach in which a pre-processing applying a non-linear transformation to the covariates is used to capture the hidden data structure. To this end, we approximate the sets representing the propagated uncertainty for the pre-processed features empirically. To exploit the empirical uncertainty sets, we propose a greedy and optimistic clustering (GOC) algorithm that finds better feature candidates over such sets, yielding more condensed clusters. As an important application, we apply the GOC algorithm to synthetic datasets of the orbital properties of stars generated through our numerical simulation mimicking the formation process of the Milky Way. The GOC algorithm demonstrates an improved performance in finding sibling stars originating from the same dwarf galaxy. These realistic datasets have also been made publicly available.