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
© 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.
Parent publication ISBN978-2-87587-014-8
ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Is part of publicationESANN 2015 : Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
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