Analysis of anomalies in random permutations using recurrent neural networks
- This paper is about detecting the difference between fully-random and semi-random shuffleing data sets, with the use of unsupervised learning algorithms. Because of the limits of the k-means algorithm alone, a recurrent autoencoder is used for feature extraction to improve the results of k-means. In the next step the autoencoder alone is used for clustering.
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Erstautor Fries, M. Sc. Zweitautor Prof. Dr. Haffner
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| Author: | Fabian Fries, Ernst Georg Haffner |
|---|---|
| URN: | urn:nbn:de:hbz:tr5-746 |
| Document Type: | Working Paper |
| Language: | English |
| Date of OPUS upload: | 2022/08/11 |
| Date of first Publication: | 2022/08/15 |
| Publishing University: | Hochschule Trier |
| Release Date: | 2022/08/15 |
| Tag: | anomalies in permutations; recurrent neural networks |
| GND Keyword: | Neuronales Netz |
| Page Number: | 4 |
| First Page: | 1 |
| Last Page: | 4 |
| Departments: | FB Technik |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
