Stop using the Elbow Method

A common challenge we face when performing clustering with K-Means is to find the optimal number of clusters. Naturally, the celebrated and popular Elbow method is the technique that most data scientists use to solve this particular problem. In this post, we are going to learn a more precise and less subjective approach to help us find the optimal number of clusters, the silhouette score analysis. In another post, I provide a thorough explanation of the K-Means algorithm, its subtleties, (centroid initialization, data standardization, and the number of clusters), and some pros and cons. There, I also explain when and how to use the Elbow Method.