A batch version of the SOM for symbolic data

Kohonen´s self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. In general, the SOM neural network is constructed as a learning algorithm for numerical data. However, except these numeric data, there are many other data types such as symbolic data. Thus, Yang et al. proposed a new SOM algorithm to treat symbolic data. In order to speed up the learning efficiency, in this paper we are interested in considering a batch learning SOM to treat symbolic data. Therefore, a new batch SOM algorithm, called a batch symbolic SOM (BS-SOM), is proposed to deal with symbolic data. Finally, we apply the BS-SOM to some real examples. The results show feasibility of our BS-SOM in real applications.