DEvIANT: discovering significant exceptional (dis-)agreement within groups

Adnene Belfodil, Wouter Duivesteijn, Marc Plantevit, Sylvie Cazalens, Philippe Lamarre

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
Subtitle of host publicationEuropean Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I
EditorsUlf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Place of PublicationCham
PublisherSpringer Nature
Number of pages18
ISBN (Electronic)978-3-030-46150-8
ISBN (Print)978-3-030-46149-2
Publication statusPublished - 2020
Event2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019) - Wurzburg, Germany
Duration: 16 Sep 201920 Sep 2019
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11906 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2019)
Abbreviated titleECML PKDD 2019
Internet address


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