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

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

doi:10.1007/978-3-030-46150-8_1

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 proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
Subtitle of host publication	European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I
Editors	Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Place of Publication	Cham
Publisher	Springer Nature
Pages	3-20
Number of pages	18
ISBN (Electronic)	978-3-030-46150-8
ISBN (Print)	978-3-030-46149-2
DOIs	https://doi.org/10.1007/978-3-030-46150-8_1
Publication status	Published - 2020
Event	19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany Duration: 16 Sep 2019 → 20 Sep 2019 http://ecmlpkdd2019.org/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11906 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Abbreviated title	ECML PKDD 2019
Country	Germany
City	Wurzburg
Period	16/09/19 → 20/09/19
Internet address	http://ecmlpkdd2019.org/

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Belfodil, A., Duivesteijn, W., Plantevit, M., Cazalens, S., & Lamarre, P. (2020). DEvIANT: discovering significant exceptional (dis-)agreement within groups. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I (pp. 3-20). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11906 LNAI). Springer Nature. https://doi.org/10.1007/978-3-030-46150-8_1

Belfodil, Adnene ; Duivesteijn, Wouter ; Plantevit, Marc ; Cazalens, Sylvie ; Lamarre, Philippe. / DEvIANT : discovering significant exceptional (dis-)agreement within groups. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. Cham : Springer Nature, 2020. pp. 3-20 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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{\textquoteright}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.",

author = "Adnene Belfodil and Wouter Duivesteijn and Marc Plantevit and Sylvie Cazalens and Philippe Lamarre",

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Belfodil, A, Duivesteijn, W, Plantevit, M, Cazalens, S & Lamarre, P 2020, DEvIANT: discovering significant exceptional (dis-)agreement within groups. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11906 LNAI, Springer Nature, Cham, pp. 3-20, 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg, Germany, 16/09/19. https://doi.org/10.1007/978-3-030-46150-8_1

DEvIANT : discovering significant exceptional (dis-)agreement within groups. / Belfodil, Adnene; Duivesteijn, Wouter; Plantevit, Marc; Cazalens, Sylvie; Lamarre, Philippe.

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. Cham : Springer Nature, 2020. p. 3-20 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11906 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Belfodil, Adnene

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AU - Cazalens, Sylvie

AU - Lamarre, Philippe

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AB - 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.

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M3 - Conference contribution

SN - 978-3-030-46149-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Belfodil A, Duivesteijn W, Plantevit M, Cazalens S, Lamarre P. DEvIANT: discovering significant exceptional (dis-)agreement within groups. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. Cham: Springer Nature. 2020. p. 3-20. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-46150-8_1

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

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