# Distributional Clustering: A distribution-preserving clustering method

arXiv: https://arxiv.org/abs/1911.05940 · HTML: https://mlsanity.com/paper/1911.05940/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling](https://mlsanity.com/paper/2110.02858/) (2021)
- [PERCEPT: a new online change-point detection method using topological data analysis](https://mlsanity.com/paper/2203.04246/) (2022)

## Abstract (arXiv)

One key use of k-means clustering is to identify cluster prototypes which can serve as representative points for a dataset. However, a drawback of using k-means cluster centers as representative points is that such points distort the distribution of the underlying data. This can be highly disadvantageous in problems where the representative points are subsequently used to gain insights on the data distribution, as these points do not mimic the distribution of the data. To this end, we propose a new clustering method called "distributional clustering", which ensures cluster centers capture the distribution of the underlying data. We first prove the asymptotic convergence of the proposed cluster centers to the data generating distribution, then present an efficient algorithm for computing these cluster centers in practice. Finally, we demonstrate the effectiveness of distributional clustering on synthetic and real datasets.