Suppose I have 1-D data which has some outliers, I want to normalize the data to be in the range [0,1]. I tried calculating the maximum value and the minimum value as follows:
q1,q2,q3 = quartiles of the data max = q3 + (q3-q1)*1.5 min = q1 - (q3-q1)*1.5
I used the above approach because I have read that data above maximum or data less than the minimum (as calculated above is noise).
My question is: whatever I am doing, is it correct or is there any other way to achieve good results?
Thank you for helping.