  Data normalization is an essential task when modeling a classification
system. When dealing with data streams, data normalization becomes especially
challenging since we may not know in advance the properties of the features,
such as their minimum/maximum values, and these properties may change over
time. We compare the accuracies generated by eight well-known distance
functions in data streams without normalization, normalized considering the
statistics of the first batch of data received, and considering the previous
batch received. We argue that experimental protocols for streams that consider
the full stream as normalized are unrealistic and can lead to biased and poor
results. Our results indicate that using the original data stream without
applying normalization, and the Canberra distance, can be a good combination
when no information about the data stream is known beforehand.
