Abstract
We consider an exploratory approach to multivariate outlier detection based on the neural network introduced by Kohonen and generally known as the self-organizing map. Working in cooperation with each other, a few meaningful 2-D images (readily derived from the trained map) are shown to provide an inexpensive, partly interactive framework where various types of outlying patterns can be detected. Some robust aspects of the key underlying notion of self-organization are discussed.
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