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Cluster analysis
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Analysis 1 focused on distinguishing self-testing study profiles in 266 undergraduates using k-means clump analysis.
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K-means clustering algorithm is designed to water parting the samples into subsets with the goal that maximizes the intra-subset law of similarity and inter-subset unsimilarity where the law of similarity measures the human relationship between two samples.
Feature extraction includes 2 parts: feature building and feature pick.
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For rq2, k-means cluster analytic thinking generated a 2-cluster model with fundamental f-statistic value contributions across all 8 composite variables.
Further consolidation of the profoundness information into the original feature ground substance has substantial upshot in reducing the noise existed fashionable the clustering results given by k-means clustering alone.
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