Visual Anomaly Detection in Educational Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00090792" target="_blank" >RIV/00216224:14330/16:00090792 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-44748-310" target="_blank" >http://dx.doi.org/10.1007/978-3-319-44748-310</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-44748-310" target="_blank" >10.1007/978-3-319-44748-310</a>

Result language
angličtina
Original language name
Visual Anomaly Detection in Educational Data
Original language description
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.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings
ISBN
9783319447476
ISSN
0302-9743
e-ISSN
—
Number of pages
10
Pages from-to
99-108
Publisher name
Springer International Publishing
Place of publication
Bulgaria
Event location
Bulgaria
Event date
Jan 1, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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