Newbie alert to data science and ML. I'm learning Supervised and Unsupervised learning at the moment and Supervised learning is easy to digest and I can relate to a lot of practical use cases. Unsupervised learning is where I just couldn't correlate to a the real-world use cases (although I found numerous of quotes where people say they use it for Customer Segmentation, Fraud Detection etc).
For argument sake, I'll just quote a sample taken from one of the MS Azure Studio examples.
The dataset contains Countries and their average protein intakes in various forms of food).
When this dataset is run through a KMeans algorithm, it creates 3 clusters and fits the country names in these clusters.
So in this specific example what is the problem I'm trying to solve?
Am I looking to find similar countries based on protein intake habits?
Am I creating groups based on the given dataset and then there is a human intelligence to qualify these groups (or clusters) to say "Vegetarian Rich Countries", "Red Meat Rich Countries" etc. Then when a new country comes we predict whether this country falls in which cluster?
In this case, there is an intermediate human intelligence is needed in the workflow which requires labelling the cluster (as opposed to labelling each datapoint in classification). Is this a correct understanding?