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This algorithm is popularly known in gene expression data clustering in which the expression values are closely related. SOM adapts a neural network template with a single layer in which data is split over neurons as shown in Figure 2.5. As every neuron is connected to every other neuron and is associated with a reference vector, the data objects are plotted to the nearest reference vectors to form quality clusters [24].

SOM’s are highly effective in mapping high dimensional data. The representation of data in the form of map provides quick visualization and interpretation [24].