TY  - UNPD
A1  - Fries, Fabian
A1  - Haffner, Ernst Georg
T1  - Analysis of anomalies in random permutations using recurrent neural networks
N2  - Abstract: 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.

Introduction: In the last years, machine learning has been used more and more in different areas and it is also appropriate for for pattern recognition in data. Random data is characterized through the missing of defined patterns. Permutations without repetitions have the highest amount of entropy for a sequence of its length, which is similar to random data according to Andrei Kolmogorov, who states that random data have the highest amount of information and can’t be compressed. Therefore, this paper analyses the difference between random permutations and good shuffled permutations, which have some remaining patterns left. This is done via a recurrent autoencoder.
KW  - anomalies in permutations
KW  - recurrent neural networks
KW  - Maschinelles Lernen
KW  - Mustererkennung
KW  - Datensatz
KW  - Permutation
KW  - Algorithmus
KW  - k-Means-Algorithmus
KW  - Neuronales Netz
Y1  - 2022
UR  - https://hst.opus.hbz-nrw.de/frontdoor/index/index/docId/74
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:tr5-746
SP  - 1
EP  - 4
ER  -