bigMap: Big Data Mapping with Parallelized t-SNE

TL;DR

bigMap presents an enhanced large-scale data clustering method combining a parallelized t-SNE, adaptive density estimation, and watershed segmentation, all integrated into an R package for effective visualization and analysis.

Contribution

It introduces a parallelized t-SNE implementation and a novel adaptive density estimation technique tailored for large datasets, improving clustering accuracy and scalability.

Findings

Significantly reduces t-SNE computational time for large datasets

Provides more accurate density estimates in low-dimensional embeddings

Enables effective clustering and visualization of big data

Abstract

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.