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I will try to implement a k-means algorithm over this dataset:

Team_Categorical  CreditAmount_Numeric   Retired?_Binary
       A                 15.3                   1
       B                 12                     0
       C                 6.2                    1

In order to apply k-means algorithm I need to transform the categorical variables into numeric. So I will apply binning transformaton over the Team_Categorical field. So I will have this dataset:

A    B   C  CreditAmount_Numeric   Retired?_Binary
1    0   0          15.3                 1
0    1   0          12                   0
0    0   1          6.2                  1

My question is: Should I transform CreditAmount_Numeric to binary too?

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To answer your question, you need not transform the numeric to binary variable(you meant binning right?).

I will try to explain it with the above example:

snapshot Let's start with row-1 where A = 1 , B =0 , C=0 so it means that you are talking about A. So the value of CreditAmount_Numeric belongs to A and the retired binary value also belongs to A. Similarly, you can deduce for the rest records.

Conclusion is that you need not do any transformation on your numeric variable.

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  • $\begingroup$ But for example, in K-Means it will be calculating centroides with binaries and numerics? Like making the average with all the numerics? $\endgroup$ – John_Rodgers Mar 1 '18 at 8:17
  • $\begingroup$ yes it takes centroides of the numeric variables and perform the clustering $\endgroup$ – Toros91 Mar 1 '18 at 8:24

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