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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Application of Statistical K-Means Algorithm for University Academic Evaluation.

Daohua Yu1, Xin Zhou2, Yu Pan2

  • 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

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|July 27, 2022
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Summary
This summary is machine-generated.

This study introduces a novel statistical K-means (SKM) algorithm for academic performance evaluation. The SKM algorithm offers more accurate clustering by mapping data to Riemannian space, outperforming traditional methods.

Keywords:
academic evaluationclusteringstatistical K-meansstatistical manifold

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Area of Science:

  • Higher Education Research
  • Data Science
  • Statistical Modeling

Background:

  • Academic evaluation is crucial in global higher education.
  • Existing methods often rely on subjective indicator weighting.
  • There's a need for objective and data-driven evaluation techniques.

Purpose of the Study:

  • To investigate a new method for academic performance evaluation.
  • To address limitations of traditional subjective weighting approaches.
  • To leverage big data for more accurate university rankings.

Main Methods:

  • Utilizing the statistical K-means (SKM) algorithm for clustering academic data.
  • Mapping evaluation data from Euclidean to Riemannian space.
  • Employing K-means on statistical manifolds for university evaluation.

Main Results:

  • The SKM algorithm demonstrated superior performance in clustering.
  • Simulation experiments showed advantages over traditional K-means and GMM.
  • The method proved effective on both university data and a UCI ML dataset.

Conclusions:

  • The SKM algorithm provides an objective and adaptable approach to academic evaluation.
  • Mapping data to Riemannian space enhances clustering accuracy.
  • This method effectively utilizes big data for reliable academic performance assessment.