Data clustering has been a major research and application topic in data mining. The self-organizing map (SOM) has been widely applied to tasks including multivariate data visualization and clustering. SOM not only quantizes the input data but also enables visual display of data, a property that does not exist in most clustering algorithms. In the past decade many developments have reported towards to mining useful information from a trained map. Most of them use post-processing methods in a two- or three-step procedure to enable finding clusters as contiguous regions on the map. The basic assumption relies on the data density approximation by the neurons through the unsupervised learning. By analyzing neighboring neurons and their relations and activities it is possible to draw, in many cases, the geometry of clusters. This paper discusses issues related to SOM clustering and segmentation with morphological image processing methods, such as filtering and watershed transform. It also briefly reviews SOM clustering related literature, such as surface-based and clustering (hierarchical and partitioning) algorithms. A new gradient-based visualization matrix is presented and results of benchmark data sets are described.
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