Abstract
Self-organizing maps (SOMs) have been applied for practical data analysis, in the contexts of exploratory data analysis (EDA) and data mining (DM). Many SOM-based EDA and DM techniques require that descriptive labels be applied to a SOM’s neurons. Several techniques exist for labeling SOM neurons in a supervised fashion, using classification information associated with a set of labeling data examples. However, classification information is often unavailable, necessitating the use of unsupervised labeling approaches that do not require pre-classified labeling data. This paper surveys existing unsupervised neuron labeling techniques. A novel unsupervised labeling algorithm, namely unsupervised weight-based cluster labeling, is described and critically discussed. The proposed method labels emergent neuron clusters using sub-labels built from statistically significant weights. Visualizations of the labelings produced by a prototype of the proposed approach are presented.
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van Heerden, W.S., Engelbrecht, A.P. (2013). Unsupervised Weight-Based Cluster Labeling for Self-Organizing Maps. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_5
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DOI: https://doi.org/10.1007/978-3-642-35230-0_5
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