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It is often necessary in science to identify samples that have features in common. For example, one might wish to find those NMR spectra in a large database that have similar patterns of resonances or identify samples amongst a large number of specimens of river water that analysis shows have similar biochemical oxygen demand, heavy metals concentration, organochlorine content, and so on. The determination of relationships among samples is a task to which Artificial Intelligence is increasingly being applied. In this paper, we investigate the Self-Organizing Map (SOM), whose role is to perform just this kind of task; in other words, to cluster data samples so as to reveal the relationships that exist among them. The self-organizing map is a method, which, unusually, combines a mathematical foundation with an intuitive interpretation. We will describe how a simple SOM operates, what kinds of data may be analyzed using one, and how a computer program to run a SOM can be written by anyone-whether student or teacher-with modest programming skills. Portions of sample source code are included in this paper, and program listings for the examples that are discussed are available in the supporting materials. The supporting files can also be used to see the maps in operation. |
