Image measurement with self‐organizing maps: A tentative application to Austrian tour operators

The neurocomputing methodology of Self‐Organizing Maps may contribute to finding a radically parsimonious representation of perception spaces in low‐dimensional image spaces.

Abstract

Image analysis faces data reduction problems when deriving low‐dimensional image spaces (‘perceptual maps’) from multidimensional profile data. The neurocomputing methodology of Self‐Organizing Maps may contribute to finding a radically parsimonious representation. The principles of SOM methodology are shown in a case study on the company images of nine Austrian tour operators.