bigMap: Big Data Mapping with Parallelized t-SNE
This work addresses the need for efficient clustering of large datasets, but it is incremental as it builds on existing methods like t-SNE and watershed algorithms.
The authors tackled the problem of unsupervised clustering for large-scale structured data by introducing a three-step protocol that includes a parallelized t-SNE for dimensionality reduction, an adaptive kernel density estimation, and a watershed algorithm for segmentation, resulting in the bigMap R package with tools for assessment.
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.