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Efficient exploratory clustering analyses in large-scale exploration processes.

, , , and . VLDB J., 31 (4): 711-732 (2022)

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Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features., , , and . ICEIS (2), page 364-373. SCITEPRESS, (2022)Efficient exploratory clustering analyses in large-scale exploration processes., , , and . VLDB J., 31 (4): 711-732 (2022)Learning from Past Observations: Meta-Learning for Efficient Clustering Analyses., , and . DaWaK, volume 12393 of Lecture Notes in Computer Science, page 364-379. Springer, (2020)Towards a Collaborative Repository for the Documentation of Service-Based Antipatterns and Bad Smells., , , , , and . ICSA Companion, page 95-101. IEEE, (2019)Efficient Exploratory Clustering Analyses with Qualitative Approximations., , and . EDBT, page 337-342. OpenProceedings.org, (2021)AutoML4Clust: Efficient AutoML for Clustering Analyses., , and . EDBT, page 343-348. OpenProceedings.org, (2021)ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis., , , and . Proc. ACM Manag. Data, 1 (2): 144:1-144:26 (2023)Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification., , , , and . VLDB J., 32 (5): 1037-1064 (September 2023)SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis., , , , and . ICEIS (1), page 419-428. SCITEPRESS, (2023)Approach to Synthetic Data Generation for Imbalanced Multi-class Problems with Heterogeneous Groups., , , and . BTW, volume P-331 of LNI, page 329-351. Gesellschaft für Informatik e.V., (2023)