when the data is from different types (numerical and categorical)
of course euclidean distance alone or hamming distance alone can't help.
so i have 2 approaches:
standardize all the data with min_max scaling, now all the numeric data are between [0,1] now we can use euclidean distance alone
calculate the euclidean distance for numeric data and calculate hamming distance for categorical data, and then combine both distances(with weights)
my question is:
1-are my 2 approaches correct?if yes, then which is better?how can i combine the distances(choosing the weight for each feature)? is there an implementation of the second approach in sklearn in python?
