Wintersemester 2023/24

Clustering is a fundamental, unsupervised machine-learning technique that can uncover relevant patterns within the data's concealed structures. In this seminar, our primary goal is to explore visualization-driven methods for understanding clustering results and concepts. Students will individually choose a distinct clustering theme (e.g., cluster partitions and overlapping clusters, hierarchical clusters, visualizing text data clusters, assessing clustering ensembles, portraying co-clustering outcomes, evaluating fuzzy clustering) and conduct research to gain knowledge in this domain. Finally, everybody will compare a few visualization approaches in depth. Please note that introductory sessions will provide students with a foundation in clustering.

Offered as "VIS-Sem-M: Masterseminar Informationsvisualisierung" to Master's Students with an additional level of complexity.