I have about a half-dozen variables, each of which can have anywhere from three to ten outcomes. I have to measure the degree of separation/similarity between rows.
Either we can do some sort of weighted system for each variable and then go by a cutoff percent, or we can apply some sort of classification technique?
I was thinking k-NN, but it's not designed for categorical data. I know that there is an extension for categorical data, but what's the difference between percentage/weighted similarity?
Open to thoughts or clarifications of my assumptions. Thank you.
EDIT: Adding some example data (not exactly the same but simplified)
TIME RANGE | TYPE | REGION | MANAGER | ....... | ....... | ===========|==========|==========|=============|=========|=========| SHORT | STOCK | US | JP MORGAN | ... | ... | SHORT | STOCK | INTL | BOA | ... | ... | LONG | BOND | US | GOLDMAN | ... | ... | SHORT | BOND | US | BOA | ... | ... | MEDIUM | BOND | INTL | BOA | ... | ... | LONG | STOCK | INTL | JP MORGAN | ... | ... | MEDIUM | BOND | INTL | GOLDMAN | ... | ... | SHORT | BOND | US | GOLDMAN | ... | ... | MEDIUM | BOND | US | GOLDMAN | ... | ... | MEDIUM | STOCK | INTL | GOLDMAN | ... | ... |
EDIT: Domain knowledge, want to find out how similar each row was to another. So we would have some sort of key/identifier, and we would only want to match two of them. I guess we can assign some sort of coded rating to each row? But if we are just comparing two specific rows, then how would we go about it?
EDIT: I believe this would be some sort of unsupervised learning. Hence, the dependent variable would not necessarily be categorical. The independent ones WOULD be.