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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.

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  • $\begingroup$ Can you show us some data and explain little bit more by what you mean with ``degree of separation/similarity between rows''? $\endgroup$
    – Vladislavs Dovgalecs
    Commented Apr 1, 2015 at 16:32
  • $\begingroup$ Hi. Added it. Please let me know if it helps. Thanks. $\endgroup$
    – vdiddy
    Commented Apr 1, 2015 at 16:47
  • $\begingroup$ Thanks for the data. Would you like to classify the rows in some predefined classes (assumes you have labels) or cluster the data? $\endgroup$
    – Vladislavs Dovgalecs
    Commented Apr 1, 2015 at 16:51
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    $\begingroup$ Can you also provide some domain knowledge, e.g. how would you read this data and classify/cluster it? How would you group a handful of rows? $\endgroup$
    – Vladislavs Dovgalecs
    Commented Apr 1, 2015 at 17:01
  • $\begingroup$ So basically we want to see how "similar" each of the rows are. I mean we could just do percentages (3/5 match so it's 60%), etc. but it doesn't seem to add much technical weight. $\endgroup$
    – vdiddy
    Commented Apr 1, 2015 at 17:18

1 Answer 1

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Multinomial logit is a technique to classify purely categorical dependent variables, and permits some measure of distance between outcomes. But I doubt this is an exhaustive list, simply one I am somewhat familiar with.

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