This seminar focuses on advanced data mining algorithms for processing big, complex and unstructured data. It mainly concerns recommendation systems, dimensionality reduction with neighborhood embedding, temporal data mining and decision support systems. In recommendation systems, various approaches from simple collaborative filtering to advanced matrix factorization are presented and discussed in the context of their practical relevance, concerning not only the popular MSE or MAE measures, but also the coverage, diversity, and novelty of recommendations. In temporal data mining, beside the analysis of regular time series with machine learning methods, such as Support Vector Regression and Neural Networks, unstructured temporal data are studied. Additional topics may concern unstructured datasets, such as irregular multidimensional time series, GPS tracks or medical images.
