correlation matrix
how can I use the correlation matirx operator between attributes with different value types specifically between a nominal and a real/integer attribute?
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Hi @Sunnyboy_nh,
the Correlation Matrix operator can only handle numerical attributes, this would require a rank correlation.
But the operator works fine with any numerical values, so there is no need to convert between real and integers.
Best,
David
What would it mean to calculate the correlation between a nominal and a numerical attribute?
You should convert nominal to numerical first, generally using dummy coding, and then compute the correlation on the resulting data.
You should convert nominal to numerical first, generally using dummy coding, and then compute the correlation on the resulting data.
Thanks Telcontar120 for reply.
Yes sure i also did a conversion to binominal first but doing that I get in the result matrix of correlation matrix instead of one converted attribute all of a sudden multiple of that same attribute each time wirh slightly different values which my matrix rows and column number which i don'need and i don't underestand .....!
What do exactly mean wirh dummy coding ?
Yes sure i also did a conversion to binominal first but doing that I get in the result matrix of correlation matrix instead of one converted attribute all of a sudden multiple of that same attribute each time wirh slightly different values which my matrix rows and column number which i don'need and i don't underestand .....!
What do exactly mean wirh dummy coding ?
Hi David A.
Yes you are right. I don't want to do correlstion between two integer and real but to do correlation between integer or real # and polynominal ?
Yes you are right. I don't want to do correlstion between two integer and real but to do correlation between integer or real # and polynominal ?
After dummy coding, you will have a series of numerical (0/1) attributes for each nominal value. So for example if you started with a nominal attribute with 3 values (A,B,C), then you will end up with 3 new numerical attributes: value=A, value=B, and value=C. Each one will be a (0/1) numerical variable that tells you if that example had that value or not. These can be run in a correlation matrix with other numerical attributes. Check out the tutorial process for the Nominal to Numerical operator if you want to see this in action inside RapidMiner.
Thanks Telcontar120 for your further explanstion on dummy coding.....hence I have used there another option " unique integer" instead of dummy coding and prompt it wieked tge way I wanted without extra unwanted new values of the same atteibute.
You should be very careful using the unique integer coding option! If your underlying nominal attribute is not scalar and ordinal, then this method won't make a lot of sense. For example, imagine you have a nominal attribute with the values (red, blue, green, purple). Unique integer coding will internally transform this into a numerical as 1=blue, 2=green, 3=purple, 4=red. Would it make sense to then use this integer in any kind of numerical calculation such as a correlation? Certainly not! So while it may be annoying that dummy coding creates extra attributes, this really is the correct way of handling nominal attributes that are not inherently in some kind of numerically ordered categories.
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After dummy coding, you will have a series of numerical (0/1) attributes for each nominal value. So for example if you started with a nominal attribute with 3 values (A,B,C), then you will end up with 3 new numerical attributes: value=A, value=B, and value=C. Each one will be a (0/1) numerical variable that tells you if that example had that value or not. These can be run in a correlation matrix with other numerical attributes. Check out the tutorial process for the Nominal to Numerical operator if you want to see this in action inside RapidMiner.