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A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs.

, , , , and . NFMCP@PKDD/ECML, volume 11948 of Lecture Notes in Computer Science, page 3-20. Springer, (2019)

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A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs., , , , and . NFMCP@PKDD/ECML, volume 11948 of Lecture Notes in Computer Science, page 3-20. Springer, (2019)Combining instance and feature neighbours for extreme multi-label classification., , , and . Int. J. Data Sci. Anal., 10 (3): 215-231 (2020)Mining cohesive patterns in sequences and extreme multi-label classification. University of Antwerp, Belgium, (2020)A Neighbourhood-based Location- and Time-aware Recommender System., , and . ORSUM@RecSys, volume 3303 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)PETSC: pattern-based embedding for time series classification., , and . Data Min. Knowl. Discov., 36 (3): 1015-1061 (2022)Pattern-Based Anomaly Detection in Mixed-Type Time Series., , , , and . ECML/PKDD (1), volume 11906 of Lecture Notes in Computer Science, page 240-256. Springer, (2019)Mining Top-k Quantile-based Cohesive Sequential Patterns., , and . SDM, page 90-98. SIAM, (2018)Efficiently mining cohesion-based patterns and rules in event sequences., , and . Data Min. Knowl. Discov., 33 (4): 1125-1182 (2019)Efficient pattern-based anomaly detection in a network of multivariate devices., , and . CoRR, (2023)Efficiently Mining Frequent Representative Motifs in Large Collections of Time Series., , and . IEEE Big Data, page 66-75. IEEE, (2023)