My question deals with what is the right way to normalize my data. My data consists 6 features, all together representing a state in an environment for reinforcement learning. My goal is to cluster states with KMeans, so of course I need to normalize values first.
(X axis is feature value, Y axis is number of appearances. if you wonder where are features 3 and 5, they are kinda look like normal)
I wonder what is the right way to normalize each feature (or all of them together) to use kmeans.
I tried to apply f-score (all features toghther, not one by one) but I wonder if it is the right away. The minimum value I get is -4.6 (in feature 2, as you can see it has values around zero) with maximum of around ~1. Are there better suggestions what can I do?