| BAYER, Jaroslav, Hana BYDŽOVSKÁ, Jan GÉRYK, Tomáš OBŠÍVAČ a Lubomír POPELÍNSKÝ. Improving the Classification of Study-related Data through Social Network Analysis. In Z. Kotásek, J. Bouda, I. Černá, L. Sekanina, T. Vojnar, D. Antoš. Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science. first. Brno: Brno University of Technology, 2011, s. 3-10. ISBN 978-80-214-4305-1. |
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@inproceedings{954083,
author = {Bayer, Jaroslav and Bydžovská, Hana and Géryk, Jan and Obšívač, Tomáš and Popelínský, Lubomír},
address = {Brno},
booktitle = {Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science},
edition = {first},
editor = {Z. Kotásek, J. Bouda, I. Černá, L. Sekanina, T. Vojnar, D. Antoš},
keywords = {Data Mining; Weka; Pajek; Social Network Analysis},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-214-4305-1},
pages = {3-10},
publisher = {Brno University of Technology},
title = {Improving the Classification of Study-related Data through Social Network Analysis},
year = {2011}
}
TY - JOUR
ID - 954083
AU - Bayer, Jaroslav - Bydžovská, Hana - Géryk, Jan - Obšívač, Tomáš - Popelínský, Lubomír
PY - 2011
TI - Improving the Classification of Study-related Data through Social Network Analysis
PB - Brno University of Technology
CY - Brno
SN - 9788021443051
KW - Data Mining
KW - Weka
KW - Pajek
KW - Social Network Analysis
N2 - The Information System of Masaryk University (IS MU) hosts applications utilized for managing study-related records, e-learning tools and those facilitating communication inside the University. This paper is concerned with improvement of results obtained with Excalibur, a tool for mining study-related data, when linked data have been added. These data describe social dependencies gathered from e-mail and discussion boards conversation. We first describe results based on the original (non-linked) data that are periodically saved into Excalibur data warehouse. Then focus on extraction of social dependencies namely relations and communication among students. We describe a method for feature extraction from the social dependencies. New features were explored by social network analysis and visualization tool Pajek and added to the original data. We show that such enriched data allows to significantly improve results obtained with data mining methods. We demonstrate this general technique on different tasks that concern classification of successful/non-successful students at Faculty of Informatics MU.
ER -
BAYER, Jaroslav, Hana BYDŽOVSKÁ, Jan GÉRYK, Tomáš OBŠÍVAČ a Lubomír POPELÍNSKÝ. Improving the Classification of Study-related Data through Social Network Analysis. In Z. Kotásek, J. Bouda, I. Černá, L. Sekanina, T. Vojnar, D. Antoš. \textit{Memics 2011 - Seventh Doctoral Workshop on Mathematical and Engeneering Methods in Computer Science}. first. Brno: Brno University of Technology, 2011, s.~3-10. ISBN~978-80-214-4305-1.
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