#Machine Learning Cluster Detection in a Nanoindentation Dataset

This project explores various unsupervised learning algorithms to detect clusters in a shales nanoindentation dataset from the Paper[1] published in 2021. The dataset was provided by the paper's corresponding author, Professor Guoping Zhang, College of Engineering, UMass Amherst.

The dataset contains nanoindentation data for 0.3 and 4 um displacement depth, each is comprised of Hardness (H), Elasticity (E), and Stiffness (S) measurements.

Various unsupervised cluster detection machine learning algorithms are explored in analysis of 1D (Hardness, Young's Modulus, and Stiffness, separately), 2D (Hardness vs. Young's Modulus), and 3D (Hardness vs. Young's Modulus vs. Stiffness) data. Analysis is done for depths of 0.3 and 4um.

[1] Luo, S., et al, 2021, Big Data Nanoindentation and Analytics Reveal the Multi-Staged, Progressively-Homogenized, Depth-Dependent Upscaling of Rocks’ Properties. Rock Mechanics and Rock Engineering, 54:1501–1532.