Mining Implications From Data

Ahcène Boubekki; Daniel Bengs

Mining Implications From Data

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

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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. Hrsg. / Thomas Seidl; Marwan Hassani; Christian Beecks. Rheinisch-Westfälische Technische Hochschule Aachen, 2014. S. 205-216 (CEUR Workshop Proceedings; Band 1226).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Harvard

Boubekki, A & Bengs, D 2014, Mining Implications From Data. in T Seidl, M Hassani & C Beecks (Hrsg.), Proceedings of the LWA 20 14 Workshops: KDML, IR and FGWM: Aachen, Germany, September 8 - 10, 2014. CEUR Workshop Proceedings, Bd. 1226, Rheinisch-Westfälische Technische Hochschule Aachen, S. 205-216, 16th LWA 2014 Workshops KDML, IR and FGWM, Aachen, Deutschland, 08.09.14. <http://ceur-ws.org/Vol-1226/paper32.pdf>

APA

Boubekki, A., & Bengs, D. (2014). Mining Implications From Data. In T. Seidl, M. Hassani, & C. Beecks (Hrsg.), Proceedings of the LWA 20 14 Workshops: KDML, IR and FGWM: Aachen, Germany, September 8 - 10, 2014 (S. 205-216). (CEUR Workshop Proceedings; Band 1226). Rheinisch-Westfälische Technische Hochschule Aachen. http://ceur-ws.org/Vol-1226/paper32.pdf

Vancouver

Boubekki A, Bengs D. Mining Implications From Data. in Seidl T, Hassani M, Beecks C, Hrsg., Proceedings of the LWA 20 14 Workshops: KDML, IR and FGWM: Aachen, Germany, September 8 - 10, 2014. Rheinisch-Westfälische Technische Hochschule Aachen. 2014. S. 205-216. (CEUR Workshop Proceedings).

Bibtex

@inbook{2e6ec87800ed4a219d4275b34d97c141,

title = "Mining Implications From Data",

abstract = "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.",

keywords = "Informatics, Business informatics",

author = "Ahc{\`e}ne Boubekki and Daniel Bengs",

year = "2014",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "Rheinisch-Westf{\"a}lische Technische Hochschule Aachen",

pages = "205--216",

editor = "Seidl, {Thomas } and Marwan Hassani and Christian Beecks",

booktitle = "Proceedings of the LWA 20 14 Workshops: KDML, IR and FGWM",

address = "Germany",

note = "16th LWA 2014 Workshops KDML, IR and FGWM ; Conference date: 08-09-2014 Through 10-09-2014",

}

RIS

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 -

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