DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups

Abstract : We strive to find contexts (i.e., subgroups of entities) under which exceptional (dis-)agreement occurs among a group of individuals , in any type of data featuring individuals (e.g., parliamentarians , customers) performing observable actions (e.g., votes, ratings) on entities (e.g., legislative procedures, movies). To this end, we introduce the problem of discovering statistically significant exceptional contextual intra-group agreement patterns. To handle the sparsity inherent to voting and rating data, we use Krippendorff's Alpha measure for assessing the agreement among individuals. We devise a branch-and-bound algorithm , named DEvIANT, to discover such patterns. DEvIANT exploits both closure operators and tight optimistic estimates. We derive analytic approximations for the confidence intervals (CIs) associated with patterns for a computationally efficient significance assessment. We prove that these approximate CIs are nested along specialization of patterns. This allows to incorporate pruning properties in DEvIANT to quickly discard non-significant patterns. Empirical study on several datasets demonstrates the efficiency and the usefulness of DEvIANT. Technical Report Associated with the ECML/PKDD 2019 Paper entitled: "DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups".
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Submitted on : Monday, July 1, 2019 - 4:57:10 PM
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Adnene Belfodil, Wouter Duivesteijn, Marc Plantevit, Sylvie Cazalens, Philippe Lamarre. DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups. [Research Report] LIRIS UMR CNRS 5205. 2019. ⟨hal-02161309v3⟩

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