Clustering for high dimension, low sample size data using distance vectors

In high dimension, low sample size (HDLSS) contexts, it's not always true that a closeness of two objects means information of a hidden cluster structure. We point out the important fact that not the closeness, but values of distances, have information of the hidden cluster structure in high dimensional space. Based on this fact, we propose an efficient and simple clustering method, called distance vector clustering, for high dimension, low sample size data.