| GÉRYK, Jan, Lubomír POPELÍNSKÝ and Jozef TRIŠČÍK. Visual Anomaly Detection in Educational Data. Online. In Christo Dichev, Gennady Agre. Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings. Bulgaria: Springer International Publishing, 2016, p. 99-108. ISBN 978-3-319-44747-6. Available from: https://dx.doi.org/10.1007/978-3-319-44748-310. |
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@inproceedings{1353940,
author = {Géryk, Jan and Popelínský, Lubomír and Triščík, Jozef},
address = {Bulgaria},
booktitle = {Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings},
doi = {http://dx.doi.org/10.1007/978-3-319-44748-310},
editor = {Christo Dichev, Gennady Agre},
keywords = {Visual analytics; Academic analytics; Anomaly detection; Temporal data; Educational data mining},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Bulgaria},
isbn = {978-3-319-44747-6},
pages = {99-108},
publisher = {Springer International Publishing},
title = {Visual Anomaly Detection in Educational Data},
year = {2016}
}
TY - JOUR
ID - 1353940
AU - Géryk, Jan - Popelínský, Lubomír - Triščík, Jozef
PY - 2016
TI - Visual Anomaly Detection in Educational Data
PB - Springer International Publishing
CY - Bulgaria
SN - 9783319447476
KW - Visual analytics
KW - Academic analytics
KW - Anomaly detection
KW - Temporal data
KW - Educational data mining
N2 - This paper is dedicated to finding anomalies in short multivariate time series and focus on analysis of educational data. We present ODEXEDAIME, a new method for automated finding and visualising anomalies that can be applied to different types of short multivariate time series. The method was implemented as an extension of EDAIME, a tool for visual data mining in temporal data that has been successfully used for various academic analytics tasks, namely its Motion Charts module. We demonstrate a use of ODEXEDAIME on analysis of computer science study fields.
ER -
GÉRYK, Jan, Lubomír POPELÍNSKÝ and Jozef TRIŠČÍK. Visual Anomaly Detection in Educational Data. Online. In Christo Dichev, Gennady Agre. \textit{Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings}. Bulgaria: Springer International Publishing, 2016, p.~99-108. ISBN~978-3-319-44747-6. Available from: https://dx.doi.org/10.1007/978-3-319-44748-310.
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