Knowledge Discovery and Data Mining (2019/2020)

(only for Elite Master Program) This lecture provides an introduction about the basics on automatic and semi-automatic knowledge discovery from electronic data repositories. Topics include the general process of knowledge discovery as well as the major tasks and approaches employed in the steps of the process. The lecture introduces general techniques for modeling data within feature spaces and provides techniques to specify object similarity. Furthermore, general techniques in the area of data mining and pattern search are discussed, such as lazy learning, density-based clustering, k-medoid clustering, outlier and anomaly detection, apriori algorithm, FP-growth, suffix trees and gSpan.