Suppose we have N events and the goal is to somehow group these N events in D different groups. Then each of those groups will be sent to an optimization algorithm and will return a value. The lower the value the better.

Assuming we do not have access to the optimization algorithm, how can I use machine learning to study how to group these N events in D different groups?

Example. For sake of simplicity, assume we have 100 events and I have two options:

• Group each of them individually and run the optimization process on each of them. I get an overall metric X
• Group all the events in a single group and run the optimization process. I get a metric Y.

The goal is to group these events such as we get the lowest error (the groups do not need to have the same number of events, it could be whatever).

Any clue?