Unsupervised Algorithms

This project provides some examples of unsupervised algorithms in machine learning. In these techniques, we need to infer the properties of the observations without the help of an output variable or supervisor. We review two methods: k-means and hierarchical clustering. Then we use some data from Kaggle for applying these techniques to produce a customer segmentation. The platform that we use is R. Because of the number of observations, we are going to use a parallel process for improving the execution times using the snow package.