Density-Based Outlier Detection Benchmark on Synthetic Data (Thesis): Unterschied zwischen den Versionen
Keine Bearbeitungszusammenfassung |
Keine Bearbeitungszusammenfassung |
||
Zeile 5: | Zeile 5: | ||
|betreuer=Georg Steinbuss | |betreuer=Georg Steinbuss | ||
|termin=Institutsseminar/2019-06-21 Zusatztermin | |termin=Institutsseminar/2019-06-21 Zusatztermin | ||
|kurzfassung= | |kurzfassung=Outlier detection is a popular topic in research, with a number of different approaches developed. Evaluating the effectiveness of these approaches however is a rather rarely touched field. The lack of commonly accepted benchmark data most likely is one of the obstacles for running a fair comparison of unsupervised outlier detection algorithms. This thesis compares the effectiveness of twelve density-based outlier detection algorithms in nearly 800.000 experiments over a broad range of algorithm parameters using the probability density as ground truth. | ||
}} | }} |
Aktuelle Version vom 17. Juni 2019, 12:55 Uhr
Vortragende(r) | Lena Witterauf | |
---|---|---|
Vortragstyp | Bachelorarbeit | |
Betreuer(in) | Georg Steinbuss | |
Termin | Fr 21. Juni 2019 | |
Vortragsmodus | ||
Kurzfassung | Outlier detection is a popular topic in research, with a number of different approaches developed. Evaluating the effectiveness of these approaches however is a rather rarely touched field. The lack of commonly accepted benchmark data most likely is one of the obstacles for running a fair comparison of unsupervised outlier detection algorithms. This thesis compares the effectiveness of twelve density-based outlier detection algorithms in nearly 800.000 experiments over a broad range of algorithm parameters using the probability density as ground truth. |