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Benchmarking Big Data Technologies for Energy Procurement Efficiency.

, , , and . AMCIS, Association for Information Systems, (2017)

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Benchmarking Big Data Technologies for Energy Procurement Efficiency., , , and . AMCIS, Association for Information Systems, (2017)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)Initializing k-Means Efficiently: Benefits for Exploratory Cluster Analysis., and . OTM Conferences, volume 11877 of Lecture Notes in Computer Science, page 146-163. Springer, (2019)DATA-IMP: An Interactive Approach to Specify Data Imputation Transformations on Large Datasets., , , and . CoopIS, volume 13591 of Lecture Notes in Computer Science, page 55-74. Springer, (2022)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)Targeting customers for an optimized energy procurement., , , and . Comput. Sci. Res. Dev., 32 (1-2): 225-235 (2017)Methods for enhanced exploratory clustering analyses.. University of Stuttgart, Germany, (2021)Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization., , , and . Proc. VLDB Endow., 17 (11): 2880-2892 (July 2024)Automatic Selection of Analytic Platforms with ASAP-DM., , and . SSDBM, page 220-225. ACM, (2021)