How to handle list features in clustering?

Question

I have a dataset where one of the features is a list. Example:

[
 {
  ... other features ...
  X: ['joey', 'monica', 'ross']
 },
 {
  ... other features ...
  X: ['chandler', 'rachel', 'joey', 'phoebe']
 }
  ... more records ...
]

Feature X in the example can be of different lengths(let's say [0,50]). The domain for the values in X is of 200 different values.

How should I handle X?

Thanks.

EDIT: More information about what X is. I'm trying to cluster failed software tests. Each test might fail in a different component. X is a list of failed components. What I'm trying to understand is how to represent this list of failed components in the vector of each data point.

Answer 1

You essentially want to have a numerical value to represent the similarity of the lists of two distinct objects. There are a number of metrics to deal with that, eg the Jaccard Index or Dice's Coefficient.

Which one is the best for you depends on the exact properties and your use case.