Efficient Discovery of Meaningful Outlier Relationships
ACM/IMS Transactions on Data Science, pp. 1-33, 2019.
We propose PODS (Predictable Outliers in Data-trendS), a method that, given a collection of temporal data sets, derives data-driven explanations for outliers by identifying meaningful relationships between them. First, we formalize the notion of meaningfulness, which so far has been informally framed in terms of explainability. Next, si...More
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