||To analyse very high dimensional data, or large data volumes, we
study random projection. Since hierarchically clustered data can be scaled in
one dimension, seriation or unidimensional scaling is our primary objective.
Having determined a unidimensional scaling of the multidimensional data
cloud, this is followed by clustering. In many past case studies we carried
out such clustering, using the Baire, or longest common preﬁx, metric and,
simultaneously, ultrametric. In this paper, we examine properties of the seriation,
and of the induction of the clustering on the data summarization, through
seriation. Simulations are described as well as a small, illustrative example
using Fisher’s iris data.