| Semester: | Sommersemester 2015 |
| Veranstalter: | Prof. Seidl |
| Bemerkungen: | The course addresses the problem of analyzing large databases of complex data, which are high-dimensional feature vectors, connected objects from annotated networks, or data streaming from dynamic data sources. The content of the course comprises the following topics: HighD: Mining high-dimensional data - Challenges and solutions for subspace clustering, projected clustering, multi-view clustering, outlier detection. Streams: Mining dynamic stream data - Challenges and solutions for clustering, classification, concept drift detection. Graphs: Mining graph and network data - Challenges and solutions for data analysis and similarity models. Only the lectures are recorded not the exercises |