When normalizing a dataset, the resulting data will have a minimum value of 0 and a maximum value of 1. However, the dataset we work with in data mining is typically a sample of a population. Therefore, the minimum and maximum for each of the attributes in the population are unknown. Samples from the population may be added to the dataset over time, and the attribute values for these new objects may then lie outside those you have seen so far. One possibility to handle new minimum and maximum values is to periodically renormalize the data after including the new values. Your task is to think of a normalization scheme that does not require you to renormalize all of the data. Your normalization approach has to fulfill all of the following requirements: - all values (old and new) have to lie in the range between 0 and 1 - no transformation or renormalization of the old values is allowed Describe your normalization approach.