https://sdq.kastel.kit.edu/api.php?action=feedcontributions&user=Uxegn%40student.kit.edu&feedformat=atomSDQ-Institutsseminar - Benutzerbeiträge [de]2024-03-29T10:32:55ZBenutzerbeiträgeMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Density-Based_Outlier_Detection_Benchmark_on_Synthetic_Data_(Thesis)&diff=1009Density-Based Outlier Detection Benchmark on Synthetic Data (Thesis)2019-06-17T10:55:20Z<p>Uxegn@student.kit.edu: </p>
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<div>{{Vortrag<br />
|vortragender=Lena Witterauf<br />
|email=uxegn@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Georg Steinbuss<br />
|termin=Institutsseminar/2019-06-21 Zusatztermin<br />
|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.<br />
}}</div>Uxegn@student.kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Density-Based_Outlier_Detection_Benchmark_on_Synthetic_Data_(Thesis)&diff=997Density-Based Outlier Detection Benchmark on Synthetic Data (Thesis)2019-06-10T18:43:02Z<p>Uxegn@student.kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Lena Witterauf |email=uxegn@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Georg Steinbuss |termin=Institutsseminar/2019-06-21 |…“</p>
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<div>{{Vortrag<br />
|vortragender=Lena Witterauf<br />
|email=uxegn@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Georg Steinbuss<br />
|termin=Institutsseminar/2019-06-21<br />
|kurzfassung=tbd<br />
}}</div>Uxegn@student.kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Density-Based_Outlier_Detection_Benchmark_on_Synthetic_Data&diff=839Density-Based Outlier Detection Benchmark on Synthetic Data2019-01-15T15:01:29Z<p>Uxegn@student.kit.edu: </p>
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<div>{{Vortrag<br />
|vortragender=Lena Witterauf<br />
|email=uxegn@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Georg Steinbuss<br />
|termin=Institutsseminar/2019-01-18<br />
|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.<br />
In this thesis the effectiveness of density-based outlier detection algorithms (such as KNN, LOF <br />
and related methods) on entirely synthetically generated data are compared, using its underlying density as ground truth.<br />
}}</div>Uxegn@student.kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Density-Based_Outlier_Detection_Benchmark_on_Synthetic_Data&diff=838Density-Based Outlier Detection Benchmark on Synthetic Data2019-01-15T14:59:55Z<p>Uxegn@student.kit.edu: </p>
<hr />
<div>{{Vortrag<br />
|vortragender=Lena Witterauf<br />
|email=uxegn@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Georg Steinbuss<br />
|termin=Institutsseminar/2019-01-18<br />
|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.<br />
In this thesis the effectiveness of density-based outlier detection algorithms (such as knn, lof and related methods) are compared on entirely synthetically generated data, using its underlying density as ground truth.<br />
}}</div>Uxegn@student.kit.eduhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Density-Based_Outlier_Detection_Benchmark_on_Synthetic_Data&diff=837Density-Based Outlier Detection Benchmark on Synthetic Data2019-01-15T14:58:27Z<p>Uxegn@student.kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Lena Witterauf |email=uxegn@student.kit.edu |vortragstyp=Proposal |betreuer=Georg Steinbuss |termin=Institutsseminar/2019-01-18 |kurzfa…“</p>
<hr />
<div>{{Vortrag<br />
|vortragender=Lena Witterauf<br />
|email=uxegn@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Georg Steinbuss<br />
|termin=Institutsseminar/2019-01-18<br />
|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.<br />
In this thesis the effectiveness of density based outlier detection algorithms (such as knn, lof and related methods) are compared on entirely synthetically generated data, using its underlying density as ground truth.<br />
}}</div>Uxegn@student.kit.edu