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• W3025489849 abstract "In cognitive diagnostic assessment (CDA), clustering analysis is an efficient approach to classify examinees into attribute-homogeneous groups. Many researchers have proposed different methods, such as the nonparametric method with Hamming distance, K-means method, and hierarchical agglomerative cluster analysis, to achieve the classification goal. In this paper, according to their responses, we introduce a spectral clustering algorithm (SCA) to cluster examinees. Simulation studies are used to compare the classification accuracy of the SCA, K-means algorithm, G-DINA model and its related reduced cognitive diagnostic models. A real data analysis is also conducted to evaluate the feasibility of the SCA. Some research directions are discussed in the final section." @default.
• W3025489849 created "2020-05-21" @default.
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• W3025489849 date "2020-05-15" @default.
• W3025489849 modified "2023-10-01" @default.
• W3025489849 title "Spectral Clustering Algorithm for Cognitive Diagnostic Assessment" @default.
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• W3025489849 doi "https://doi.org/10.3389/fpsyg.2020.00944" @default.
• W3025489849 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7242625" @default.
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