# Some Clustering-based Change-point Detection Methods Applicable to High Dimension, Low Sample Size Data

arXiv: https://arxiv.org/abs/2111.14012 · HTML: https://mlsanity.com/paper/2111.14012/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [Interval Estimation of Coefficients in Penalized Regression Models of Insurance Data](https://mlsanity.com/paper/2410.01008/) (2024)
- [Distribution-free inference for LightGBM and GLM with Tweedie loss](https://mlsanity.com/paper/2507.06921/) (2025)
- [Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting](https://mlsanity.com/paper/2508.10055/) (2025)

## Abstract (arXiv)

Detection of change-points in a sequence of high-dimensional observations is a very challenging problem, and this becomes even more challenging when the sample size (i.e., the sequence length) is small. In this article, we propose some change-point detection methods based on clustering, which can be conveniently used in such high dimension, low sample size situations. First, we consider the single change-point problem. Using k-means clustering based on some suitable dissimilarity measures, we propose some methods for testing the existence of a change-point and estimating its location. High-dimensional behavior of these proposed methods are investigated under appropriate regularity conditions. Next, we extend our methods for detection of multiple change-points. We carry out extensive numerical studies to compare the performance of our proposed methods with some state-of-the-art methods.