# Robust and sparse k-means clustering for high-dimensional data

arXiv: https://arxiv.org/abs/1709.10012 · HTML: https://mlsanity.com/paper/1709.10012/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

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

- [Adaptively Robust and Sparse K-means Clustering](https://mlsanity.com/paper/2407.06945/) (2024)
- [A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks](https://mlsanity.com/paper/2105.03289/) (2021)
- [Evolution of K-means solution landscapes with the addition of dataset outliers and a robust clustering comparison measure for their analysis](https://mlsanity.com/paper/2306.14346/) (2023)
- [Hyperoctant Search Clustering: A Method for Clustering Data in High-Dimensional Hyperspheres](https://mlsanity.com/paper/2503.07917/) (2025)

## Abstract (arXiv)

In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is a need for a clustering method which is capable of revealing the group structure in data containing both outliers and noise variables without any pre-knowledge. In this paper, we propose a k-means-based algorithm incorporating a weighting function which leads to an automatic weight assignment for each observation. In order to cope with noise variables, a lasso-type penalty is used in an objective function adjusted by observation weights. We finally introduce a framework for selecting both the number of clusters and variables based on a modified gap statistic. The conducted experiments on simulated and real-world data demonstrate the advantage of the method to identify groups, outliers, and informative variables simultaneously.