Abstract
Users can be grouped by clustering taking into account multiple features simultaneously. The efficiency of the method depends on the quality of obtained user groups. In the paper, the validity index, which indicates cluster quality taking into account accuracy of models, built on clusters, is considered. We assume that cluster elements are expressed by vectors of nominal values and that cluster representation is in the form of likelihood matrix. As group models frequent patterns will be considered. The performance of the proposed index will be investigated on the basis of the experiments carried out for groups of e-learning system users described by their learning styles. Comparisons to relative criteria of cluster validity will be presented.
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Zakrzewska, D. (2013). Validation of Clustering Techniques for User Group Modeling. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_71
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DOI: https://doi.org/10.1007/978-3-319-00969-8_71
Publisher Name: Springer, Heidelberg
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