Some clustering-based change-point detection methods applicable to high dimension, low sample size data

Detection of change-points in a sequence of high dimensional observations is a 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 a 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 and analyze a real data set to compare the performance of our proposed methods with some state-of-the-art methods.