Density-Based Outlier Detection Benchmark on Synthetic Data

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Version vom 15. Januar 2019, 17:01 Uhr von Lena Witterauf (Diskussion | Beiträge)
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Vortragende(r) Lena Witterauf
Vortragstyp Proposal
Betreuer(in) Georg Steinbuss
Termin Fr 18. Januar 2019
Kurzfassung Outlier detection algorithms are widely used in application fields such as image processing and fraud detection. Thus, during the past years, many different outlier detection algorithms were developed. While a lot of work has been put into comparing the efficiency of these algorithms, comparing methods in terms of effectiveness is rather difficult. One reason for that is the lack of commonly agreed-upon benchmark data.

In this thesis the effectiveness of density-based outlier detection algorithms (such as KNN, LOF and related methods) on entirely synthetically generated data are compared, using its underlying density as ground truth.