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Self-Organizing Map (SOM) is a neural network model which is used to obtain atopology-preserving mapping from the (usually high dimensional) input/featurespace to an output/map space of fewer dimensions (usually two or three in orderto facilitate visualization). Neurons in the output space are connected witheach other but this structure remains fixed throughout training and learning isachieved through the updating of neuron reference vectors in feature space.Despite the fact that growing variants of SOM overcome the fixed structurelimitation they increase computational cost and also do not allow the removalof a neuron after its introduction. In this paper, a variant of SOM is proposedcalled AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand createsa more flexible structure where neuron positions are dynamically altered duringtraining and on the other hand tackles the drawback of having a predefined gridby allowing neuron addition and/or removal during training. Experiments usingmultiple literature datasets show that the proposed method improves trainingperformance of SOM, leads to a better visualization of the input dataset andprovides a framework for determining the optimal number and structure ofneurons.

AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization