Clustering Models Applied to the Energy Sector - Part 1
Marcelo Guarido, Daniel O. Trad, David J. Emery
This lab is the first part of the "Clustering Models" series, and was inspired by the work of Smith K. J. (2017), which shows a clustering application to the energy industry by creating a seismic velocity auto-picking on a semblance pannel. Clustering models are widely used on different applications, and they have the goal to group your data into similarity groups. There are a large selection of models, each one with its particularity, and in this lab we will understand how to implement clustering work flows. During the lab, we will present the definitions of different clustering models, and show how to select and implement them in Python using the package Scikit-Learn.