Finding the K in K-Means Clustering

Six ways to find the elusive k in the K-means..

A couple of weeks ago, here at The Data Science Lab we showed how Lloyd’s algorithm can be used to cluster points using k-means with a simple python implementation. We also produced interesting visualizations of the Voronoi tessellation induced by the clustering. At the end of the post we hinted at some of the shortcomings of this clustering procedure. The basic k-means is an extremely simple and efficient algorithm. However, it assumes prior knowledge of the data in order to choose the appropriate K. Other disadvantages are the sensitivity of the final clusters to the selection of the initial centroids and the fact that the algorithm can produce empty clusters. In today’s post, and by popular request, we are going to have a look at the first question, namely how to find the appropriate K to use in the k-means clustering procedure.