Hierarchical Clustering Analysis with SOM Networks

This work presents a neural network model for the clustering analysis of data based on Self Organizing Maps (SOM). The model evolves during the training stage towards a hierarchical structure according to the input requirements. The hierarchical structure symbolizes a specialization tool that provides refinements of the classification process. The structure behaves like a single map with different resolutions depending on the region to analyze. The benefits and performance of the algorithm are discussed in application to the Iris dataset, a classical example for pattern recognition.

Authors:

• Diego Ordonez
• Carlos Dafonte
• Minia Manteiga
• Bernardino Arcayy

Keywords:

• Neural networks
• Self-organizing feature maps
• Hierarchicalsystems
• Pattern clustering methods.

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