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The Chemical Educator

ISSN: 1430-4171 (electronic version)

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Abstract Volume 5 Issue 4(2000) pp 196-204
DOI 10.1333/s00897000400a [old prefix 10.1007/]

Undergraduate Projects in the Application of Artificial Intelligence to Chemistry. II Self-Organizing Maps

Hugh Cartwright

Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, England

Published online: 1 August 2000

Abstract. 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.

Key Words:  Computers in Chemistry; AI; programming; java

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Article in PDF format (57 KB) HTML format

Supporting Materials:

Figures (HTML) 10.1007/s00897000400b
Code (HTML) 10.1007/s00897000400c

Issue date: August 1, 2000

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