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Actionable Subgroup Discovery and Urban Farm Optimization.

, , , and . IDA, volume 12080 of Lecture Notes in Computer Science, page 339-351. Springer, (2020)

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Découverte de sous-groupes à partir de données séquentielles par échantillonnage et optimisation locale., , and . EGC, volume E-35 of RNTI, page 153-164. Éditions RNTI, (2019)Anytime mining of sequential discriminative patterns in labeled sequences., , , and . Knowl. Inf. Syst., 63 (2): 439-476 (2021)Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets., , , , , and . ICDM (Workshops), page 1221-1230. IEEE, (2023)DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection., , , , and . CoRR, (2023)Anytime Subgroup Discovery in High Dimensional Numerical Data., , , and . DSAA, page 1-10. IEEE, (2021)On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Report., , , and . ESEC/SIGSOFT FSE, page 1820-1831. ACM, (2023)SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences., , , and . DSAA, page 81-90. IEEE, (2019)A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League., , and . CoG, page 267-274. IEEE, (2020)Rule Discovery in Labeled Sequential Data: Application to Game Analytics. (Découverte de règles pour séquences labélisées: application à l'analyse de données de jeux vidéos).. University of Lyon, France, (2020)Actionable Subgroup Discovery and Urban Farm Optimization., , , and . IDA, volume 12080 of Lecture Notes in Computer Science, page 339-351. Springer, (2020)