The talk will present new developments in the field of neural gas-like networks and will show how this comparatively young and powerful class of neural networks can be applied successfully to data analysis and clustering. Due to their flexible structure (and unlike SOM, which are fixed to predefined lattices) these algorithms are able to preserve the topological structure of the data to be analyzed (i.e. the data determines the topology of the neural network). This proves helpful in clustering for market segmentation, among others.

The performance of the relevant algorithms in pattern recognition and clustering is demonstrated using synthetic as well as real marketing data. The applications discussed include market segmentation, market basket analysis, and missing value imputation.

Keywords: Clustering, data analysis, marketing, neural gas network

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