How to perform clustering on a dataset containing TRUE/FALSE values in Python?

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My dataset contains columns describing abilities of certain characters, filled with True/False values. There are no empty values. My ultimate goal is to make groups of characters with similar abilities. And here's the question:

  1. Should i change True/False values to 1 and 0? Or there's no need for that?
  2. What clustering model should i use? Is KMeans okay for that?
  3. How do i interpret the results (output)? Can i visualize it?

The thing is i always see people perform clustering on numeric datasets that you can visualize and it looks much easier to do. With True/False i just don't even know how to approach it.

Thanks.

data-science cluster-analysis dataset python
1 Answers
  1. In general there is no need to change True/False to 0/1. This is only necessary if you want to apply a specific algorithm for clustering that cannot deal with boolean inputs, like K-means.
  2. K-means is not a preferred option. K-means requires continuous features as input, as it is based on computing distances, like many clustering algorithms. So no boolean inputs. And although binary input (0-1) works, it does not compute distances in a very meaningful way (many points will have the same distance to each other). In case of 0-1 data only, I would not use clustering, but would recommend tabulating the data and see what cells occur frequently. If you have a large data set you might use the Apriori algorithm to find cells that occur frequently.
  3. In general, a clustering algorithm typically returns a cluster number for each observation. In low-dimensions, this number is frequently used to give a color to an observation in a scatter plot. However, in your case of boolean values, I would just list the most frequently occurring cells.
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