To understand and transform data so that it can be used as input for a machine learning algorithm is part of the KDD process. Intelligent methods can be used in this phase already, such as learning how to replace missing values. Since the quality of the achieved learning result depends crucially on an adequate representation of the data, this phase is equally important as mining itself.
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