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Entropy 2019, 21(3), 219; https://doi.org/10.3390/e21030219

Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data

1
Department of Computational Medicine and Bioinformatics, University of Michigan, NCRC 10-A108, 2800 Plymouth Rd, Ann Arbor, MI 48109-2800, USA
2
Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
3
Shenzhen Research Institute of Big Data, Shenzhen 518172, China
*
Author to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 8 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

The aim of using atypicality is to extract small, rare, unusual and interesting pieces out of big data. This complements statistics about typical data to give insight into data. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. We therefore base the atypicality criterion on codelength. In a prior paper we developed the methodology for discrete-valued data, and the current paper extends this to real-valued data. This is done by using minimum description length (MDL). We develop the information-theoretic methodology for a number of “universal” signal processing models, and finally apply them to recorded hydrophone data and heart rate variability (HRV) signal. View Full-Text
Keywords: atypicality; minimum description length; big data; codelength atypicality; minimum description length; big data; codelength
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sabeti, E.; Høst-Madsen, A. Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data. Entropy 2019, 21, 219.

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