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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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):License LogoCreative Commons - CC BY - Namensnennung 4.0 International