Automatic data classification by a hierarchy of self-organizing maps

Clustering is the process by which discrete objects are assigned to groups that have similar characteristics. Self-organizing maps (SOM) have been widely used as a data visualization tool. Some of their advantages include information compression and density estimation while trying to preserve the topological and metric relationships of the primary data items. For using SOM as a clustering tool additional procedures are required to interpret the mapping obtained through unsupervised learning. Costa and Netto (1999) described the usage of image analysis and mathematical morphology to find automatically regions of similar neurons and their borders. The purpose of this paper is to enhance the clustering process in order to detail the underlying structure obtained in a first trial. Groups of neurons associated to clusters are further subdivided in new sub-networks, generating a tree-like structure of SOMs. Differently to other hierarchical SOM approaches, the number of sub-nets for a given SOM in a given height of the tree is not specified in advance. The process can be seen as a dynamic strategy for cluster discovery.