A growing Bayesian self-organizing map for data clustering

An extended Bayesian self-organizing map (BSOM) learning process is proposed, called the growing BSOM (GBSOM). It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. It can automatically terminate and find the optimal number of neurons to represent the given dataset during the learning process. In this paper, three synthetic datasets and one real dataset are used to test the proposed algorithm, and three stopping criteria are used to automatically terminate the learning process, which are Bayesian information criterion (BIC) and two clustering validity indices: DB-Index and SV-Index. According to the results, using BIC as stopping criterion is better than using DB-Index and SV-Index as stopping criteria.