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The artificial neural networks (ANNs) have demonstrated remarkable performance across diverse domains to handle different challenges and problems and in this research the Self-Organizing Maps neural network for un-supervised clustering are used for different datasets to evaluate their versatility and performance. The main goal of this research is to present the obtained results after using unsupervised neural networks, in this case: Self-Organizing Maps. The datasets that are used in this research are the Iris dataset (which has 3 classes of a type of an iris plant of 50 instances each), Wine Quality dataset (which includes the red wine and white wine samples) and the Australian Credit Card Applications dataset to identify good and bad applicants.

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Self-organizing Maps as Neural Models for Different Unsupervised Clustering Problems

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  • 发表日期：

    2025年

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  • Julio C. Mónica,
  • Patricia Melin,
  • Daniela Sánchez

摘要

The artificial neural networks (ANNs) have demonstrated remarkable performance across diverse domains to handle different challenges and problems and in this research the Self-Organizing Maps neural network for un-supervised clustering are used for different datasets to evaluate their versatility and performance. The main goal of this research is to present the obtained results after using unsupervised neural networks, in this case: Self-Organizing Maps. The datasets that are used in this research are the Iris dataset (which has 3 classes of a type of an iris plant of 50 instances each), Wine Quality dataset (which includes the red wine and white wine samples) and the Australian Credit Card Applications dataset to identify good and bad applicants.