The Self-Organizing Map (SOM) is a clustering algorithm that is used to map a multi-dimensional dataset onto a (typically) two-dimensional surface. This surface (a map) is an ordered interpretation of the probability distribution of the available genes/samples of the input dataset. SOMs have been used extensively in many domains, including the exploratory data analysis of gene expression patterns.
There are two particularly useful purposes for this: visualization and cluster analysis. Visualization has typically been a difficult matter for high-dimensional data. SOMs can be used to explore the groupings and relations within such data by projecting the data on to a two-dimensional image that clearly indicates regions of similarity. Even if visualization is not the goal of applying SOM to a dataset, the clustering ability of the SOM is very useful.
Performing a SOM Experiment
Creating a SOM Plot
Tutorial 4: Self-Organizing Maps