Meine Dissertation trägt den Titel "Methods for Enhanced Exploratory Clustering Analyses".
Clustering ist von zentraler Bedeutung für grundlegende explorative Aufgaben. Verschiedene Domänen, wie Computer Vision, Bildsegmentierung, Information Retrieval sowie geschäftliche Zwecke basieren auf Clustering-Techniken. Allerdings benötigen insbesondere unerfahrene Analysten umfangreiche Unterstützung, um wertvolle Clustering-Ergebnisse zu erzielen.
In meiner Dissertation stelle ich neuartige Ansätze zur Unterstützung von (unerfahrenen) Analysten vor, um in kurzer Zeit vielversprechende Clustering-Ergebnisse zu erzielen. Anstatt explorative Clustering-Analysen durch mehr Rechenleistung zu adressieren, ist es mein Ziel, durchdachte Lösungen zu entwickeln, die auf vorhandener Hardware arbeiten können.
Meine weiteren Forschungsinteressen liegen in den Bereichen Meta-Learning, Big Data, explorative Datenanalyse und Deep Learning.
Profile
Publikationen
Fritz, M., Gang, S., Schwarz, H. 2021. “Automatic Selection of Analytic Platforms with ASAP-DM,” in Proceedings of the International Conference on Scientific and Statistical Database Management (SSDBM 2021).
Fritz, M., Tschechlov, D., and Schwarz, H. 2021. “Efficient Exploratory Clustering Analyses with Qualitative Approximations,” in Proceedings of the International Conference on Extending Database Technology (EDBT 2021).
Tschechlov, D., Fritz, M., and Schwarz, H. 2021. “AutoML4Clust: Efficient AutoML for Clustering Analyses,” in Proceedings of the International Conference on Extending Database Technology (EDBT 2021).
Fritz, M., Behringer, M., and Schwarz, H. 2020. “LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets,” in Proceedings of 46th International Conference on Very Large Data Bases (VLDB 2020).
Fritz, M., Tschechlov, D., and Schwarz, H. 2020. “Learning from past observations: Meta-Learning for Efficient Clustering Analyses,” in Proceedings of 22nd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2020). Lecture Notes in Computer Science (Vol. 12393 LNCS), Springer, Cham, pp. 364-379.
Behringer, M., Hirmer, P., Fritz, M., Mitschang, B. 2020. “Empowering Domain Experts to Preprocess Massive Distributed Datasets,” in Proceedings of 23rd International Conference on Business Information Systems (BIS 2020).
Fritz, M., and Schwarz, H. 2019. “Initializing k-means efficiently: Benefits for exploratory cluster analysis,” in Proceedings of 27th International Conference on Cooperative Information Systems (CoopIS 2019). Lecture Notes in Computer Science (Vol. 11877 LNCS), Springer, Cham, pp. 146–163.
Fritz, M., Muazzen, O., Behringer, M., and Schwarz, H. 2019. “ASAP-DM: a framework for automatic selection of analytic platforms for data mining,” in Proceedings of 13th Symposium and Summer School On Service-Oriented Computing (SummerSoC 2019). SICS Software-Intensive Cyber-Physical Systems, Springer Berlin Heidelberg, pp. 1–13.
Fritz, M., Behringer, M., and Schwarz, H. 2019. “Quality-driven early stopping for explorative cluster analysis for big data,” in Proceedings of 12th Symposium and Summer School On Service-Oriented Computing (SummerSoC 2018). SICS Software-Intensive Cyber-Physical Systems (34:2–3), pp. 129–140.
Albrecht, S., Fritz, M., Strüker, J., and Ziekow, H. 2017. “Targeting customers for an optimized energy procurement: A Cost Segmentation Based on Smart Meter Load Profiles,” Computer Science - Research and Development (32:1–2), Springer Berlin Heidelberg, pp. 225–235.
Fritz, M., Albrecht, S., Ziekow, H., and Strüker, J. 2017. “Benchmarking Big Data Technologies for Energy Procurement Efficiency,” in Proceedings of the 23rd America’s Conference on Information Systems (AMCIS 2017).
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