I have data that look like this:
amount creator accounts 100 john cash, accounts payable 325 jane accounts receivable, cash 200 john tax account, accounts payable, cash
How should these data points be clustered?
Thoughts so far:
Popular, consensus answer seems to be to one-hot encode the categorical and multivalue_categorical fields, and then scale the numeric field to [0,1]. This causes two primary problems: extremely sparse/high-dimensional data (4,000 dimensions in my case), and a numeric column that is perhaps not weighted appropriately.
Attempt to apply differing algorithms to each data type and mash them together somehow. This could involve market-basket type analysis for the multivalue_categorical, k-modes for the categorical, and k-means for numeric (or k-prototypes for the categorical and numeric).
Is there any method/implementation that would allow for these three types of data to be clustered without one-hot encoding the categorical and multivalue categorical? I have looked into SOM as an unsupervised NN that performs clustering, but I haven't seen evidence that it can handle multivalue categorical.