Mining Implications From Data
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Mining Implications From Data. / Boubekki, Ahcène; Bengs, Daniel.
Proceedings of the LWA 20 14 Workshops: KDML, IR and FGWM: Aachen, Germany, September 8 - 10, 2014. ed. / Thomas Seidl; Marwan Hassani; Christian Beecks. Rheinisch-Westfälische Technische Hochschule Aachen, 2014. p. 205-216 (CEUR Workshop Proceedings; Vol. 1226).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Mining Implications From Data
AU - Boubekki, Ahcène
AU - Bengs, Daniel
N1 - Conference code: 16
PY - 2014
Y1 - 2014
N2 - Item Tree Analysis (ITA) can be used to mine deterministic relationships from noisy data. In the educational domain, it has been used to infer descriptions of student knowledge from test responses in order to discover the implications between test items, allowing researchers to gain insight into the structure of the respective knowledge space. Existing approaches to ITA are computationally intense and yield results of limited accuracy, constraining the use of ITA to small datasets. We present work in progress towards an improved method that allows for efficient approximate ITA, enabling the use of ITA on larger data sets. Experimental results show that our method performs comparably to or better than existing approaches.
AB - Item Tree Analysis (ITA) can be used to mine deterministic relationships from noisy data. In the educational domain, it has been used to infer descriptions of student knowledge from test responses in order to discover the implications between test items, allowing researchers to gain insight into the structure of the respective knowledge space. Existing approaches to ITA are computationally intense and yield results of limited accuracy, constraining the use of ITA to small datasets. We present work in progress towards an improved method that allows for efficient approximate ITA, enabling the use of ITA on larger data sets. Experimental results show that our method performs comparably to or better than existing approaches.
KW - Informatics
KW - Business informatics
UR - http://ceur-ws.org/Vol-1226/
UR - http://www.scopus.com/inward/record.url?scp=84925003708&partnerID=8YFLogxK
M3 - Article in conference proceedings
T3 - CEUR Workshop Proceedings
SP - 205
EP - 216
BT - Proceedings of the LWA 20 14 Workshops: KDML, IR and FGWM
A2 - Seidl, Thomas
A2 - Hassani, Marwan
A2 - Beecks, Christian
PB - Rheinisch-Westfälische Technische Hochschule Aachen
T2 - 16th LWA 2014 Workshops KDML, IR and FGWM
Y2 - 8 September 2014 through 10 September 2014
ER -