Data Segmentation via t-SNE, DBSCAN, and Random Forest

This research proposes a data segmentation technique which is easy to interpret and generalizes well. The technique combines t-SNE, DBSCAN, and Random Forest classifier algorithms to form an end-to-end pipeline that separates data into natural clusters and produces a characteristic profile of each cluster based on the most important features. Out-of-sample cluster labels can be inferred, and the technique generalizes well on real data sets. We describe the algorithm and provide case studies using the Iris and MNIST data sets, as well as real social media site data from Instagram. The main contributions of this work are the explicit identification of clusters from a t-SNE embedding, the cluster profiles, and the treatment of how these clusters generalize to out-of-sample data.