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Research on Clustering Algorithm Based on Improved SOM Neural Network.

Chengxiang Shi1, Xiaoqing Li1

  • 1Department of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, China.

Computational Intelligence and Neuroscience
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Self-Organizing Map (SOM) neural network algorithm for student quality evaluation. The enhanced clustering method accurately classifies students, aiding in comprehensive quality assessment.

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Area of Science:

  • Computer Science
  • Data Science
  • Educational Technology

Background:

  • Clustering algorithms are essential for sample classification in data science.
  • Increasing data complexity necessitates advanced classification techniques.
  • Traditional clustering methods face challenges with high-dimensional data.

Purpose of the Study:

  • To propose an improved Self-Organizing Map (SOM) neural network algorithm.
  • To enhance the accuracy and efficiency of clustering for student quality evaluation.
  • To address the challenges of high-dimensional data in clustering applications.

Main Methods:

  • Developed an improved SOM neural network algorithm.
  • Integrated factor analysis for dimensionality reduction in the SOM input layer.
  • Applied the algorithm to cluster analysis of comprehensive student quality data.

Main Results:

  • The improved SOM algorithm effectively evaluates students' comprehensive quality.
  • Factor analysis successfully processed high-dimensional student data, improving speed and accuracy.
  • The algorithm intuitively reflects the overall characteristics of different student types.

Conclusions:

  • The enhanced SOM neural network provides an effective tool for student quality assessment.
  • Dimensionality reduction via factor analysis optimizes SOM performance for complex datasets.
  • This approach offers a robust method for clustering and analyzing student populations.