Self-organizing map 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. It is an unsupervised learning which has both visualization and clustering properties. In general, the SOM neural network is constructed as a learning algorithm for numeric (vector) data. Although there are different SOM clustering methods for numeric data with real applications in the literature, there is less consideration in a SOM clustering for symbolic data. In this paper, we modify the SOM so that it can treat symbolic data and a so-called symbolic SOM (S-SOM) is then proposed. We first use novel structures to represent symbolic neurons. We then use a suppression concept to create a learning rule for neurons. Therefore, the S-SOM is created for treating symbolic data by embedding the novel structure and the suppression learning rule. Some real data sets are applied with the S-SOM. The experimental results show the feasibility and effectiveness of the proposed S-SOM in these real applications.