Supervised Neural Gas for Learning Vector Quantization

Villmann T, Hammer B. Supervised Neural Gas for Learning Vector Quantization. In: Polani D, Kim J, Martinetz T, eds. Proc. of the 5th German Workshop on Artificial Life. Berlin: Akademische Verlagsgesellschaft - infix - IOS Press; 2002: 9-16

Analyzing Destination Images: A Perceptual Charting Approach

Heterogeneity of perceptions is a neglected issue in market segmentation studies. Only recently parametric approaches toward modeling segmented perception-preference structures such as combined MDS and Latent Class procedures have been introduced. A completely different nonparametric method is based on topology-sensitive vector quantization (VQ) for consumers-by-brands-by-attributes data. It maps the segment-specific perceptual structures into bar charts with multiple brand positions exhibiting perceptual distinctiveness or similarity. A brief introduction into the VQ methodology is followed by a sample study on three urban destinations competing on the world travel markets. City images serve as the underlying behavioral constructs. Preferential data are based on respondents\u27 comes-closest-to-ideal-city judgments and incorporated into the perceptual positions of city profiles. Perceptual charting works on two levels of aggregation named prototypes and perceptual sub-structures. The results demonstrate how this method prevents the analyst from drawing erroneous conclusions due to uncontrolled aggregation