Data Clustering Analysis using Self-Organizing Maps with 3-D Output Grids

The self-organizing map (SOM) has been widely used as a software tool for visualization of high-dimensional data. Important SOM features include information compression while trying to preserve topological and metric relationship of the primary data items. The assumption of topological preservation in SOMs is not true for many data mappings involving dimension reduction. With the automation of cluster detection in SOM higher output dimensions can be used in problems involving discovery of classes in multidimensional data. This paper presents the U-array as an extension of the U-matrix for 3-D SOM output grids. The algorithm uses the watershed transform to aid the SOM segmentation.