  Clustering analysis of sequence data continues to address many applications
in engineering design, aided with the rapid growth of machine learning in
applied science. This paper presents an unsupervised machine learning algorithm
to extract defining characteristics of earthquake ground-motion spectra, also
called latent features, to aid in ground-motion selection (GMS). In this
context, a latent feature is a low-dimensional machine-discovered spectral
characteristic learned through nonlinear relationships of a neural network
autoencoder. Machine discovered latent features can be combined with
traditionally defined intensity measures and clustering can be performed to
select a representative subgroup from a large ground-motion suite. The
objective of efficient GMS is to choose characteristic records representative
of what the structure will probabilistically experience in its lifetime. Three
examples are presented to validate this approach, including the use of
synthetic and field recorded ground-motion datasets. The presented deep
embedding clustering of ground-motion spectra has three main advantages: 1.
defining characteristics the represent the sparse spectral content of
ground-motions are discovered efficiently through training of the autoencoder,
2. domain knowledge is incorporated into the machine learning framework with
conditional variables in the deep embedding scheme, and 3. method exhibits
excellent performance when compared to a benchmark seismic hazard analysis.
