Unsupervised Visual Data Clustering
Kohonen's Self-Organizing Maps

Users looking for unattended and unsupervised data clustering tool, capable of generating convincible results, will recognize strong data analysis potential of Kohonen's Self-Organizing Maps (SOMs). Kohonen maps are a tool for arranging the data points into a manageable 2D or 3D space in a way that preserves closeness.

Also known as self-organizing maps (SOM), Kohonen maps are inspired biologically. The SOM computational mechanism reflects how many scientists think the human brain organizes many-faceted concepts into its 3D structure. The SOM algorithm lays a 2D grid of "neuronal units" and assigns each data point to the unit that will "recognize" it. The assignment is made in such a way that neighboring units recognize similar data.

The result of applying a Kohonen map to a data set is a 2D plot, but Miner3D can also support 3D Kohonen maps. In this plot, data points (rows) that are similar in the chosen set of attributes will be grouped close together, while dissimilar rows will be separated by a greater distance in the plot space. This allows you, the user, to tease out salient data patterns. Self-Organizing Maps has been the data clustering method sought by many people from different areas of business and science. The new enhancement of yet powerful set of Miner3D data analysis tools further broadens its application portfolio.