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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