In this paper we describe our endeavours to explore the role
of unsupervised learning technology in profiling marine conditions. The
characterization of the marine environment with hydrographic variables
allows, for example, to make technical and health control of sea products.
However, the continuous monitoring of the environment produces
large amounts of data and, thus, new information technology tools are
needed to support decision-making. We present here a first contribution
to this area by building a tool able to represent and normalize hydrographic
conditions, cluster them using unsupervised learning methods,
and present the results to domain experts. The tool, which implements
visualization methods adapted to the problem at hand, was developed
under the supervision of specialists on monitoring marine environment
in Galicia (Spain). This software solution is promising to early identify
risk factors and to gain a better understanding of sea conditions.
Keywords: Marine Conditions, Machine Learning, Unsupervised Learning, Clustering, Hydrographic Conditions