Example "real-world" scenario:
Let's say you want to recommend sneakers to customers based on the sneakers "similar" customers own. "similar" in this case could be determined by determining how similar customers' "sneaker closets" are. This definition lends itself naturally then to clustering techniques.
Simplification of "real-world" scenario:
Cluster the keys of a multi-valued map based on their difference of keys. Those clusters should end up being collections of keys who have the most similar sets of values.
Example "real-world" scenario: Let's say you want to recommend sneakers to customers based on the sneakers "similar" customers own. "similar" in this case could be determined by determining how similar customers' "sneaker closets" are. This definition lends itself naturally then to clustering techniques.
Simplification of "real-world" scenario: Cluster the keys of a multi-valued map based on their difference of keys. Those clusters should end up being collections of keys who have the most similar sets of values.