Artificial Neuron Networks versus Cluster Analysis: A comparative study in a sample of children with spina bifida

Authors

  • Ana María López Jiménez University of Seville image/svg+xml
  • José García Luna University of Seville image/svg+xml
  • Monserrat Gómez de Terreros University of Seville image/svg+xml
  • Antonio R. García Torres I.E.S “Los Viveros” Sevilla

DOI:

https://doi.org/10.55414/hrwm0077

Keywords:

cluster analysis, artificial neural networks, unsupervised learning , k-means

Abstract

The use of Artificial Neural Networks (ANN) in data analysis in Psychology is not very frequent, The prevalence of multivariate statistical techniques is evident although the majority of these techniques can be implemented in different architectures of ANN. The lack of studies that show the advantages of certain procedures over others could explain the scarce use of ANN. In this work, we have compared the profiles of the clusters obtained using a non-hierarchical classification algorithm (k-means) and those obtained by means of a competitive net. These two procedures have been applied to a sample of children with spina bifida and who are undergoing treatment at the «Virgen del Rocío» Children's Hospital of Seville.

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Published

02/11/2001

Issue

Section

Research articles

How to Cite

López Jiménez, A. M., García Luna, J., Gómez de Terreros, M., & García Torres, A. R. (2001). Artificial Neuron Networks versus Cluster Analysis: A comparative study in a sample of children with spina bifida. Apuntes de Psicología, 19(3), 431-442. https://doi.org/10.55414/hrwm0077

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