Playing with dimensions: from Clustering, PCA, t-SNE... to Carl Sagan!

This post is an experiment combining the result of t-SNE with two well known clustering techniques: k-means and hierarchical. This will be the practical section, in R. But also, this post will explore the intersection point of concepts like dimension reduction, clustering analysis, data preparation, PCA, HDBSCAN, k-NN, SOM, deep learning...and Carl Sagan! For those who don't know t-SNE technique (official site), it's a projection technique -or dimension reduction- similar in some aspects to Principal Component Analysis (PCA), used to visualize N variables into 2 (for example). When the t-SNE output is poor Laurens van der Maaten (t-SNE's author) says: As a sanity check, try running PCA on your data to reduce it to two dimensions.