My PhD thesis is entitled "Methods for Enhanced Exploratory Clustering Analyses".
Clustering is pivotal for fundamental exploratory tasks. Several domains, such as computer vision, image segmentation, information retrieval as well as business purposes rely on clustering techniques. However, especially novice analysts require profond support in order to achieve valuable clustering results.
In my thesis, I present novel approaches to support (novice) analysts in order to achieve promising clustering results in a short time period. Instead of "throwing more machines at the problem" and thus reducing the runtime, my goal is to engineer thorough solutions, which can work on existing hardware.
My other research interests are in the area of Meta-Learning, Big Data, Exploratory Data Analysis and Deep Learning.
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).
- Analysis and Transfer of AutoML Concepts for Clustering Algorithms
- Investigation of the Effects of Autoencoders on Data Analysis Processes
- Evaluation of intermediate results in decision trees
- Feature-driven representation of clustering results