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
Päivämäärä
2015Tekijänoikeudet
© 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.
Julkaisija
ESANNISBN
978-287587014-8Emojulkaisun ISBN
978-2-87587-014-8Konferenssi
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKuuluu julkaisuun
ESANN 2015 : Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningAsiasanat
Alkuperäislähde
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-24.pdfJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/24830472
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