k-means versus Hierarchical Clustering

Now that we have expanded our understanding of how k-means clustering works, it is important to explore where hierarchical clustering fits into the picture. As mentioned in the linkage criteria section, there is some potential direct overlap when it comes to grouping data points together using centroids. Universal to all of the approaches mentioned so far, is also the use of a distance function to determine similarity. Due to our in-depth exploration in the previous chapter, we have kept using the Euclidean distance, but we understand that any distance function can be used to determine similarity.

In practice, here are some quick highlights for choosing one clustering method over another:

• Hierarchical clustering benefits from not needing to pass in an explicit "k" number of clusters apriori. This means that you can find all the potential clusters and decide which clusters make the most sense after the algorithm has completed.
• k-means clustering benefits from a simplicity perspective – oftentimes, in business use cases, there is a challenge to find methods that can be explained to non-technical audiences but still be accurate enough to generate quality results. k-means can easily fill this niche.
• Hierarchical clustering has more parameters to tweak than k-means clustering when it comes to dealing with abnormally shaped data. While k-means is great at finding discrete clusters, it can falter when it comes to mixed clusters. By tweaking the parameters in hierarchical clustering, you may find better results.
• Vanilla k-means clustering works by instantiating random centroids and finding the closest points to those centroids. If they are randomly instantiated in areas of the feature space that are far away from your data, then it can end up taking quite some time to converge, or it may never even get to that point. Hierarchical clustering is less prone to falling prey to this weakness.