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.

翻译：在一系列高维观测中检测变化点是一个非常具有挑战性的问题,当样本大小(即序列长度)很小时,这一问题就更加具有挑战性。在本条中,我们提议了一些基于集群的改变点检测方法,可以在如此高的维度、低样本大小的情况下方便地使用。首先,我们考虑单一变化点问题。使用基于某些适当的差异计量方法的k- means集群,我们提出一些方法来测试变化点的存在并估计其位置。这些拟议方法的高维度行为在适当的常规条件下得到调查。接下来,我们扩展了多位变化点的检测方法。我们进行了广泛的数字研究,以便用一些最先进的方法来比较我们拟议方法的绩效。