# How to improve Pairwise Euclidean Distance for Similarity Measure

I am trying to identify the most similar stations between two DataFrames like below:

stations      feature_1     feature_2   feature_3 ------ feature_10
------------------------------------------------------------------------
08GD008         10           1.14          98
08GE002          5           88.67         80
08MC040          8           4.61          17
08FB006          2           13.70         53
08FC003          1           37            49
08LF002         20           2.5           30


I used a pairwise Euclidean distance and identified the minimum distance for each station to select the candidate (most similar one). I used (x-mean)/std to standardize my features ( they all have equal weights). I wanted to check if there is a way to improve my method.

I thought of using PCA (principal component analysis) to use PC1 and PC2 for my distance matrix but only 60% of variability is explained within the first two components. I was wondering if there is a way to assign weights to my features ( something like coefficient of variation of each feature multiplied by all standardized values)? or something similar to PCA in this case.

## migrated from stackoverflow.comApr 2 at 22:31

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