Evaluating the performance of clustering algorithms

So far, we have built different clustering algorithms, but haven't measured their performance. In supervised learning, the predicted values with the original labels are compared to calculate their accuracy. In contrast, in unsupervised learning, we have no labels, so we need to find a way to measure the performance of our algorithms.

Getting ready

A good way to measure a clustering algorithm is by seeing how well the clusters are separated. Are the clusters well separated? Are the datapoints in a cluster that is tight enough? We need a metric that can quantify this behavior. We will use a metric called the silhouette coefficient score. This score is defined for each...