  We introduce an improved unsupervised clustering protocol specially suited
for large-scale structured data. The protocol follows three steps: a
dimensionality reduction of the data, a density estimation over the low
dimensional representation of the data, and a final segmentation of the density
landscape. For the dimensionality reduction step we introduce a parallelized
implementation of the well-known t-Stochastic Neighbouring Embedding (t-SNE)
algorithm that significantly alleviates some inherent limitations, while
improving its suitability for large datasets. We also introduce a new adaptive
Kernel Density Estimation particularly coupled with the t-SNE framework in
order to get accurate density estimates out of the embedded data, and a variant
of the rainfalling watershed algorithm to identify clusters within the density
landscape. The whole mapping protocol is wrapped in the bigMap R package,
together with visualization and analysis tools to ease the qualitative and
quantitative assessment of the clustering.
