Enhancing Visual Clustering Using Adaptive Moving Self-Organizing Maps (AMSOM)

Recent advancements in computing technology allowed both scientific and business applications to produce large datasets with increasing complexity and dimensionality. Clustering algorithms are useful in analyzing these large datasets but often fall short to provide completely satisfactory results. Integrating clustering and visualization not only yields better clustering results but also leads to a higher degree of confidence in the findings. Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation, they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is presented called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during training. Experimental evaluation on different literature datasets with diverse characteristics improves SOM training performance, leads to a better visualization of the input dataset, and provides a framework for determining the optimal number and structure of neurons as well as the optimal number of clusters.