A Self-Organizing Map is a type of Artificial Neural Networks and has been primarily used for 1) Associative Memory and 2) Data Visualization. In either cases, the main function of the SOM is to model multivariate data. This modeling corresponds to learning the statistical structure of the multivariate data in a much simpler form (typically in a 2D space). During this process, the SOM will try to keep the topological structure of the original data as much as possible. Hence, it achieves its non-linear topological mapping from the high-dimensional space to the much lower (typically 2D) space. With this topological mapping capability, it allows us to visualize complex high-dimensional data structures.

Although the original form of the SOM does provide a useful visualization mechanism to a certain extent, it falls short of requirements in Visual Analytics. Visual Analytics is a emerging discipline, and it aims to utilize Visualization and User Interfaces in order to improve an Analytical Reasoning process. In this talk, SOM's shortcomings will be explained and some possible solutions and future challenges will be presented.