Weighted Clustering of Sparse Educational Data
Abstract
Clustering as an unsupervised technique is predominantly
used in unweighted settings. In this paper, we present an efficient version
of a robust clustering algorithm for sparse educational data that takes
the weights, aligning a sample with the corresponding population, into
account. The algorithm is utilized to divide the Finnish student population
of PISA 2012 (the latest data from the Programme for International
Student Assessment) into groups, according to their attitudes and perceptions
towards mathematics, for which one third of the data is missing.
Furthermore, necessary modifications of three cluster indices to reveal an
appropriate number of groups are proposed and demonstrated.
Main Authors
Format
Conferences
Conference paper
Published
2015
Subjects
Publication in research information system
Publisher
ESANN
Original source
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-24.pdf
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201508212733Use this for linking
Parent publication ISBN
978-287587014-8
Review status
Peer reviewed
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Language
English
Is part of publication
ESANN 2015 : Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Citation
- Saarela, M., & Kärkkäinen, T. (2015). Weighted Clustering of Sparse Educational Data. In ESANN 2015 : Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 337-342). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-24.pdf
License
Copyright© The Authors 2015. Published by ESANN.