Statistical Generation of High Dimensional Data Streams with Complex Dependencies

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Version vom 14. Dezember 2018, 08:35 Uhr von Alexander Poth (Diskussion | Beiträge)
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Vortragende(r) Alexander Poth
Vortragstyp Bachelorarbeit
Betreuer(in) Edouard Fouché
Termin Fr 14. Dezember 2018
Kurzfassung The evaluation of data stream mining algorithms is an important task in current research. The lack of a ground truth data corpus that covers a large number of desireable features (especially concept drift and outlier placement) is the reason why researchers resort to producing their own synthetic data. This thesis proposes a novel framework ("streamgenerator") that allows to create data streams with finely controlled characteristics. The focus of this work is the conceptualization of the framework, however a prototypical implementation is provided as well. We evaluate the framework by testing our data streams against state-of-the-art dependency measures and outlier detection algorithms.