Top three mistakes with K-Means Clustering during data analysis

In this post, we will take a look at a few cases, where KMC algorithm does not perform well or may produce unintuitive results. All of these conditions can lead to problems with K-Means, so let's have a look. To make it easier, let's define a helper function compare, which will create and solve the clustering problem for us and then compare the results. Despite having distinct clusters in the data, we underestimated their number. As a consequence, some disjoint groups of data are forced to fit into one larger cluster.