Data Mining Algorithms II
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