Real-life databases often contain data that is ambiguous, incomplete, and noisy, which makes it difficult for many of these techniques to uncover patterns in the data. There is a need, therefore, for knowledge discovery techniques that can identify patterns under noisy conditions. When extracted patterns are used for decision support it is particularly important that they are represented in a form understandable by decision makers.
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