FP-growth on marketing data
Hello,
is it possible to apply FP-growth when the variables are polynomial? I am trying to apply the operator to this dataset, from the book Elements of Statistical Learning:
https://web.stanford.edu/~hastie/ElemStatLearn/
I am having issues because the variables are polynomial. Can FP-growth handle this data set?
Best,
Bernardo
is it possible to apply FP-growth when the variables are polynomial? I am trying to apply the operator to this dataset, from the book Elements of Statistical Learning:
https://web.stanford.edu/~hastie/ElemStatLearn/
I am having issues because the variables are polynomial. Can FP-growth handle this data set?
Best,
Bernardo
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Altair EmployeeHi @bernardo_pagnon, which data you are using from ESL textbook? Are you able to share it here? Thanks!
I tried, but I guess it is not supported here.
You got to enter here
then click on "Data" (white button on the left), and the "Marketing", and then on the hyperlink "data".
Regards,
Bernardo
Altair EmployeeThanks @bernardo_pagnon for pointing out the source data. Still I am not sure what you want to extract from the data. Did I get the wrong data (Link direct to the income survey data) with 14 demographic attributes?
https://web.stanford.edu/~hastie/ElemStatLearn/datasets/marketing.data
https://web.stanford.edu/~hastie/ElemStatLearn/datasets/marketing.info.txt
https://web.stanford.edu/~hastie/ElemStatLearn/datasets/marketing.data
https://web.stanford.edu/~hastie/ElemStatLearn/datasets/marketing.info.txt
Yes, it is the correct one. My goal was to extract relationships between info available. I guess I identified the problem: different attributes use the number 1 through 5 for instance, to represent the different polynomial values at each attribute. But RM cannot differentiate two numbers "3" in two different attributes: they represent completely different things, but RM identify them as the same.
I guess a solution would be to work with the data and generate different numbers for each attribute:
Attribute 1: 1-9
Attribute 2: 10-11
and so on.
Best,
Bernardo
Altair EmployeeYou may not have to re-code the numbers. But string "3" in income will be equivalent to the string "3" in age...
Suppose in the beginning, you have the numerical attributes loaded for marketing data, you can convert the values from numerical to polynominal. I would prefer to replace the nominal values "1","2","3" with the string values defined by data dictionary with "Map" or "Replace" operators
e.g. in gender
Suppose in the beginning, you have the numerical attributes loaded for marketing data, you can convert the values from numerical to polynominal. I would prefer to replace the nominal values "1","2","3" with the string values defined by data dictionary with "Map" or "Replace" operators
e.g. in gender
1-> Male 2-> Female https://web.stanford.edu/~hastie/ElemStatLearn/datasets/marketing.info.txt
Interesting, will try that!
Thank you so much for your time and for your patience.
Regards,
Bernardo
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Altair EmployeeYou may not have to re-code the numbers. But string "3" in income will be equivalent to the string "3" in age...
Suppose in the beginning, you have the numerical attributes loaded for marketing data, you can convert the values from numerical to polynominal. I would prefer to replace the nominal values "1","2","3" with the string values defined by data dictionary with "Map" or "Replace" operators
e.g. in gender
Suppose in the beginning, you have the numerical attributes loaded for marketing data, you can convert the values from numerical to polynominal. I would prefer to replace the nominal values "1","2","3" with the string values defined by data dictionary with "Map" or "Replace" operators
e.g. in gender
1-> Male 2-> Female https://web.stanford.edu/~hastie/ElemStatLearn/datasets/marketing.info.txt