Expect the Unexpected – On the Significance of Subgroups
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Within the field of exploratory data mining, subgroup discovery is concerned with finding regions in the data that stand out with respect to a particular target. An important question is how to validate the patterns found; how do we distinguish a true finding from a false discovery? A common solution is to apply a statistical significance test that states that a pattern is real iff it is different from a random subset.
In this paper we argue and empirically show that this assumption is often too weak, as almost any realistic pattern language specifies a set of subsets that strongly deviates from random subsets. In particular, our analysis shows that one should expect the unexpected in subgroup discovery: given a dataset and corresponding description language, it is very likely that high-quality subgroups can —and hence will— be found.
KeywordsSubgroup Discovery (SD) High-quality Subgroups Accessible Subsets Importance Sampling Estimator Subgroup Cover
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