Visual Anomaly Detection in Educational Data

D 2016

Visual Anomaly Detection in Educational Data

GÉRYK, Jan, Lubomír POPELÍNSKÝ a Jozef TRIŠČÍK

Základní údaje

Originální název

Visual Anomaly Detection in Educational Data

Autoři

GÉRYK, Jan (203 Česká republika, domácí), Lubomír POPELÍNSKÝ (203 Česká republika, garant, domácí) a Jozef TRIŠČÍK (703 Slovensko, domácí)

Vydání

Bulgaria, Artificial Intelligence: Methodology, Systems, and Applications: 17th International Conference, AIMSA 2016, Varna, Bulgaria, September 7-10, 2016, Proceedings, od s. 99-108, 10 s. 2016

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Bulharsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/16:00090792

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-44747-6

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-44748-310

UT WoS

000389020000010

Klíčová slova anglicky

Visual analytics; Academic analytics; Anomaly detection; Temporal data; Educational data mining

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2020 19:03, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Citovat

GÉRYK, Jan, Lubomír POPELÍNSKÝ a 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, s. 99-108. ISBN 978-3-319-44747-6. Dostupné z: https://dx.doi.org/10.1007/978-3-319-44748-310.

@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 a 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, s.~99-108. ISBN~978-3-319-44747-6. Dostupné z: https://dx.doi.org/10.1007/978-3-319-44748-310.

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