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Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data.

, , , , and . VAHC, page 56-57. IEEE, (2019)

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A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data., , and . J. Biomed. Informatics, (2014)Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection., , and . IUI, page 85-95. ACM, (2016)DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data., and . IEEE Trans. Vis. Comput. Graph., 20 (12): 1783-1792 (2014)Urologist use and perceptions of the EHR: Perspectives from a Surgical Specialty., , , , , , and . AMIA, AMIA, (2021)Connecting the dots in visual analysis., , and . IEEE VAST, page 123-130. IEEE Computer Society, (2009)Knowledge Compass: A Question Answering System Guiding Students with Follow-Up Question Recommendations., , , , , , , and . UIST (Adjunct Volume), page 65:1-65:4. ACM, (2023)Improving Visualization Interpretation Using Counterfactuals., , , and . CoRR, (2021)A general framework for multidimensional adaptation., and . ACM Multimedia, page 612-619. ACM, (2004)Dynamic Hierarchical Aggregation, Selection Bias Tracking, and Detailed Subset Comparison for High-Dimensional Event Sequence Data., , , , and . VAHC, page 56-57. IEEE, (2019)UnTangle: Visual Mining for Data with Uncertain Multi-labels via Triangle Map., , , and . ICDM, page 340-349. IEEE Computer Society, (2014)