Unsupervised Learning - Using the Outcome of Learning My understanding in Unsupervised Learning is that -- when you want a computer to learn on its own by examining a large dataset. The goal is to establish some form of cluster or association-based grouping of data from the given input aka &quot;Find patterns - known/unknown&quot;
So my question is - &quot;Now what do we do with it?&quot; - e.g. I have now identified a pattern using Unsupervised Learning. Do I now look for Supervised Learning to empower my Data Insights work? Or, do I make decision as a human/team/committee to do certain things based on those?
Unsupervised Learning is a collection of tools and you can use those tools for many purposes.
Sometime, it's just for data visualisation / data exploration (see UMAP for example), to better understand the problem and get the idea how to tackle it. Sometimes it will lead to business decision like building processes for a specific cluster or building submodels for specific clusters.
Sometime it helps deal with sub-problems of data-science, like outliers identification (see Hdbscan or isolation forests for example). What to do with those outliers depends on your business case.
Sometimes it helps define intermediate 'targets' to perform semi-supervised or supervised learning, like using some self-organisaed map, identify a cluster and use that cluster as a target. It help when you are not sure you have identified all your positive targets or when you don't wnat to label everything.