I'm learning about unsupervised learning and I tried to use KMeans, AgglomerativeClustering and DBSCAN on the same datase. The result was ok, they seems to work fine according silhouette_score() function in which the best score was 0.1935 from AgglomerativeClustering. It found 2 clusters by the way. The problem is I was unable to find the differences between these two clusters. I tried several plots pairing clusters 0 and 1, but the same patterns I find in one cluster I find in the other. So, my question is:
What techniques do you use to identify differences between clusters?
Since I'm learning it seems to me clustering is just part of the problem. You have to understand the differences between the clusters to recommend different products to them for example. So labelling is not enought.