# Quantile-based clustering

arXiv: https://arxiv.org/abs/1806.10403 · HTML: https://mlsanity.com/paper/1806.10403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2018.

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

- [K-expectiles clustering](https://mlsanity.com/paper/2103.09329/) (2021)

## Abstract (arXiv)

A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd's algorithm for K-means. It can be applied to large and high-dimensional datasets. It allows for within-cluster skewness and internal variable scaling based on within-cluster variation. Different versions allow for different levels of parsimony and computational efficiency. Although K-quantiles clustering is conceived as nonparametric, it can be connected to a fixed partition model of generalized asymmetric Laplace-distributions. The consistency of K-quantiles clustering is proved, and it is shown that K-quantiles clusters correspond to well separated mixture components in a nonparametric mixture. In a simulation, K-quantiles clustering is compared with a number of popular clustering methods with good results. A high-dimensional microarray dataset is clustered by K-quantiles.