Elbow Method: Choosing the Right Number of Clusters

Choosing the Right Number of Clusters - Enthought, Inc.

When I first started my machine learning journey, K-means clustering was one of the first algorithms I was introduced to – and it is still one of my favorites to this day. I was amazed at how elegant yet comprehensible the procedure was. There is something oddly satisfying about watching the cluster assignments and centroids being updated with each iteration. While K-means clustering has been tried and true since its inception in the 1950s, there is still one foundational requirement for employing this method: choosing the correct number of clusters – the K in K-means. In this month’s newsletter, we’ll explore a technique known as the elbow method to help determine the ideal number of clusters that should be chosen for a given clustering task. To conclude, we will explore another type of clustering algorithm (Affinity Propagation clustering) that does not require a predetermined number of clusters for execution.