Efficient Discovery of Meaningful Outlier Relationships

ACM/IMS Transactions on Data Science, pp. 1-33, 2019.

Cited by: 0|Bibtex|Views35|DOI:https://doi.org/10.1145/3385192
Other Links: arxiv.org|academic.microsoft.com

Abstract:

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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