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Identifying exceptional (dis) agreement between groups

, , , and . Data Mining and Knowledge Discovery, 34 (2): 394--442 (2020)

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OLAP-Sequential Mining: Summarizing Trends from Historical., , and . New Trends in Data Warehousing and Data Analysis, volume 3 of Annals of Information Systems, Springer, (2009)Mining Disjunctive Rules in Dynamic Graphs., , and . RIVF, page 1-6. IEEE, (2012)Improving the Quality of Rule-Based GNN Explanations., , , and . PKDD/ECML Workshops (1), volume 1752 of Communications in Computer and Information Science, page 467-482. Springer, (2022)Contrastive Antichains in Hierarchies., , , , and . KDD, page 294-304. ACM, (2019)Exceptional Attributed Subgraph Mining to Understand the Olfactory Percept., , , , and . DS, volume 11198 of Lecture Notes in Computer Science, page 276-291. Springer, (2018)Qu'est-ce que mon GNN capture vraiment ? Exploration des représentations internes d'un GNN., , , , and . EGC, volume E-38 of RNTI, page 159-170. Editions RNTI, (2022)Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors., , , and . IEEE Trans. Knowl. Data Eng., 25 (9): 2090-2104 (2013)Extraction de Motifs Séquentiels Multidimensionnels Clos sans Gestion d'Ensemble de Candidats., , and . EGC, volume RNTI-E-11 of Revue des Nouvelles Technologies de l'Information, page 541-546. Cépaduès-Éditions, (2008)Hype : Prise en compte des hiérarchies lors de l'extraction de motifs séquentiels multidimensionnels., , and . EDA, volume B-2 of RNTI, page 155-174. Cépaduès, (2006)When TEDDY meets GrizzLY: temporal dependency discovery for triggering road deicing operations., , , and . KDD, page 1490-1493. ACM, (2013)