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The problem of finding contrast patterns has recently attracted much attention. As a result, a number of promising methods have been proposed to capture significant differences or changes between two or more datasets. Such differences can be captured by emerging patterns and some other types of contrasts. In this paper, the authors present a framework for mining diverging patterns, a new type of contrast patterns whose frequency changes in different directions in two data sets, e.g., it changes from a relatively low to a relatively high value in one dataset, but from high to low in the other.
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