Some of the use CASES for unsupervised learning include:

Anomaly detection

Given some standard EVENT type often occurring over time, we might want to report when a nonstandard type of event occurs. For example, a security officer might want to receive notifications when a strange object (think vehicle, skater, or bicyclist) is observed on a pathway.

User segmentation

Given a SET of user’s behaviors, we might want to better understand what attributes certain users share with other users. For instance, a gaming company might cluster users based on properties like the number of hours played in a given game. The algorithm might REVEAL that casual players have very different behavior than hardcore gamers, for example, and allow the company to offer different recommendations or rewards to each player.

Topic modeling 

Given a set of documents, we might analyze the different words contained therein to see if there is some underlying relationship between them. For example, given several web pages on data ANALYTICS, a topic modeling algorithm can cluster them into pages about machine learning, SQL, streaming, and so on based on groups of words that are more common in one topic than in others.

Intuitively, it is easy to see how segmenting customers could help a platform cater better to each set of users. However, it may be hard to discover whether this set of user segments is “correct”.  For this reason, it can be difficult to determine whether a particular model is good or not.