I hope this clearly states the problem I have in hand. Here goes:
I've trained a neural network with one initial data set that was normalized in order to guarantee an equal participation of each variable in the learning process. Once the the neural network is trained I'll have new sets of data coming to be classified.
Q1: How should I proceed in terms of normalizing a new coming data set? Is there any chance that a different mean and standard deviation will make this new data set significantly different from the one I originally used to train my classifier? Can this generate "wrong" results? (of course I'm considering the new income samples to come from the same source as the original training set).
Q2: Am I supposed to follow the same normalization procedure if instead of just classifying I'm first clustering the training data set and them classifying new income samples (into the clusters/groups found by my clustering algorithm)?