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Scalable parallel co-clustering over multiple heterogeneous data types.

, , , and . HPCS, page 529-535. IEEE, (2010)

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Deviance-Aware Discovery of High-Quality Process Models., , , and . Int. J. Artif. Intell. Tools, 27 (7): 1860009:1-1860009:27 (2018)RecBoost: A Supervised Approach to Text Segmentation., , , , and . SEBD, page 220-231. (2005)Detecting DoS and DDoS Attacks through Sparse U-Net-like Autoencoders., , and . ICTAI, page 1342-1346. IEEE, (2022)Mining usage scenarios in business processes: Outlier-aware discovery and run-time prediction., , , and . Data Knowl. Eng., 70 (12): 1005-1029 (2011)Pushing More AI Capabilities into Process Mining to Better Deal with Low-Quality Logs., and . Business Process Management Workshops, volume 362 of Lecture Notes in Business Information Processing, page 5-11. Springer, (2019)Business Process Deviance Mining., and . Encyclopedia of Big Data Technologies, Springer, (2019)On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes., , and . ICEIS (1), page 206-217. SciTePress, (2015)A comorbidity-based recommendation engine for disease prediction., and . CBMS, page 6-12. IEEE Computer Society, (2010)Data Mining for Effective Risk Analysis in a Bank Intelligence Scenario., , , , and . ICDE Workshops, page 904-911. IEEE Computer Society, (2007)Discovering High-Level Performance Models for Ticket Resolution Processes., , and . OTM Conferences, volume 8185 of Lecture Notes in Computer Science, page 275-282. Springer, (2013)