# When should we choose agglomerative clustering over K-means clustering?

I was working on a clustering based model and I read about hierarchical clustering and K-Means clustering.

Under what conditions should I choose agglomerative over K-means clustering?

You can see this comparison table in sklearn, which gives some intuition about where and when each algorithm is successful:

It might be a good idea to try both and evaluate their accuracy, with an unsupervised clustering metric, like the silhouette score, to get an objective measure of their performance on a specific dataset.

Some other major differences are:

• K-means performs better for 2D & 3D spheres
• Hierarchical clustering can have reduced performance on larger datasets
• Hierarchical clustering is sensitive to outliers

To add to WBM great citation, you should use K-means over Agglomerative when your final objetive is to use the trained algorithm to make inference over new unseen observations.

I will try to illustrate this with an example:

Imagine you have 2 models kmeans and aggcls both have been trained on data that correspond to information of customers on an specific domain (you are offering different credit cards), and your task is to form groups in order to see what product might be more interested each group on, imagine you have form the same number of clusters n in both cases, among those n groups there is one specially suitable for a premium credit card since that group has huge income, large number of transactions and also have more credit experience, so when a new customer arrives you want to evaluate him in order to know whether or no you can offer him the premium product.

With the kmeans model you would only need to make a predict over the vector of characteristics of this new client to obtain the cluster this customer belongs to, whereas with aggcls you will have to retrain the algorithm with the whole data including this new observation (not very useful right?)

This happens because of the nature of each algorithm, with kmeans, you will get n centroids that can be use to make inference of new unseen data by calculating the distance between the new instance and every cluster and then assign this new observation to the nearest one. With agglomerative, you do not generate any parameter that can be applied to new observations, you have to form your clusters again.

Agglomerative clustering

K-means