How do you learn labels with unsupervised learning? In https://huyenchip.com/machine-learning-systems-design/design-a-machine-learning-system.html#design-a-machine-learning-system-dwGQI5R, I came across the sentence:

Similarly, you can use unsupervised learning to learn labels for your data, then use those labels for supervised learning.

I have never heard of the bolded part before. How exactly do you use unsupervised learning to "learn labels" for labeless data?

Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don't "learn" labels in the sense that there is some true target label we want to identify, but rather create labels and assign them to the data. An unsupervised clustering will identify natural groups in the data, and you can interpret those groups to come up with meaningful labels instead of "Cluster 1", "Cluster 2", etc. - perhaps a patient cluster represents some aspect of biology, or some group of transactions represents fraud. The clustering assigns arbitrary categorical "labels" which can be further analyzed to discern whether they represent true, meaningful classes in your data.
If you have a useful clustering, you can then use those labels in a supervised manner to train a classifier. Rather than clustering every patient or transaction dataset and hoping to find the same clusters, you can train a classifier to use cluster-discriminative  gene signatures or fraud profiles in order to directly assign the "labels" you discovered through unsupervised clustering to new data.
