A self-organizing map (SOM) or self-organizing feature map (SOFM) is a kind of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different than other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.
In this tutorial, we show how to implement the Kohonen's SOM algorithm with Tanagra. We try to assess the properties of this approach by comparing the results with those of the PCA algorithm. Then, we compare the results to those of K-Means, which is a clustering algorithm. Finally, we implement the Two-step Clustering process by combining the SOM algorithm with the HAC process (Hierarchical Agglomerative Clustering). It is a variant of the Two-Step Clustering where we combine K-Means and HAC. We observe that the HAC primarily merges the adjacent cells.
Keywords: Kohonen, self organizing map, SOM, clustering, dimensuionality reduction, k-means, hierarchical agglomerative clustering, hac, two-step clustering
Components: UNIVARIATE CONTINUOUS STAT, UNIVARIATE OUTLIER DETECTION, KOHONEN-SOM, PRINCIPAL COMPONENT ANALYSIS, SCATTERPLOT, K-MEANS, CONTINGENCY CHI-SQUARE, HAC
Tutorial: en_Tanagra_Kohonen_SOM.pdf
Dataset: waveform_unsupervised.xls
Reference:
Wikipedia, « Self organizing map », http://en.wikipedia.org/wiki/Self-organizing_map
