I have a dataset with mixed datatypes:
id amount creator accounts 1 100 jane cash 1 100 jane accounts receivable 2 200 john tax account
id can have only one creator. Each
id can have one or more accounts. For the accounts data, I am one hot encoding the accounts and then joining them into a bitmap:
id amount creator accounts 1 100 jane 110 2 200 john 001
My plan was to apply k-prototypes to the resulting dataset. K-prototypes uses k-modes and the Hamming (# of replacements) distance to calculate similarity between categorical features and the modes. I think this works well for the bitmaps with multiple 1s. However, Hamming distance does not make sense for
creator, because my assumption is that people are not more similar just because they have similar names. Therefore, I need to one hot encode the creator feature as well.
After one hot encoding, does it make sense to combine the creator features into a bitmap like the accounts? Each
id can only have creator, so the bitmaps would only have replacement counts of 0 or 1.
Alternatively, am I overthinking this? Should k-modes actually be applied directly to single-value categorical variables?