Faculty of Science – Department of Mathematics and Computer Science (IMADA) – SDU

For an evaluation study on unsupervised outlier detection and ensemble techniques, we run various methods with various parameterizations on many datasets, resulting in ca. 4.5 mio. individual experiments which are to a certain extent independent of each other and try to see what the benefits of parallelization are on this effort.