Density-Based Outlier Detection Benchmark on Synthetic Data (Thesis): Unterschied zwischen den Versionen
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|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. | ||
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Aktuelle Version vom 17. Juni 2019, 11:55 Uhr
Vortragende(r) | Lena Witterauf | |
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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. |