Weighted Clustering of Sparse Educational Data
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
Date
2015Copyright
© The Authors 2015. Published by ESANN.
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.
Publisher
ESANNISBN
978-287587014-8Parent publication ISBN
978-2-87587-014-8Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningIs part of publication
ESANN 2015 : Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKeywords
Original source
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-24.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/24830472