Abstract
The Self-organizing map (SOM) is an effective method for topologically mapping datasets. By adapting the neurons to the inputs, the network can conform to the data and form clusters. However, with the Classical SOM and growing architectures such as Growing Cells and Growing Grid, the neurons are simply points in space and do not cover entire regions of the input space. Therefore, inputs that are introduced after the network is trained need to have cluster membership determined by proximity to the trained neurons.
The ParaSOM, being a different SOM architecture, where each neuron actually covers a region of the input space, opens up possibilities for different approaches to clustering and classification. An algorithm has been proposed to take advantage of the unique characteristics of the ParaSOM. The neighbors of each neuron are evaluated by distance to determine cluster separation. Once the clusters have successfully been identified, new inputs can be introduced to effectively determine which, if any, cluster each belongs to.