Real life datasets used in marketing studies contain a lot of redundant features which may prevent data-mining techniques such as self-organizing maps from discovering relevant clusters. An extension of the batch Kohonen's algorithm is proposed in this paper to avoid the large amount of work which is required by data preprocessing if redundancy isn't treated explicitly by the training method. The proposed approach integrates a weighting of variables built on a simultaneous clustering of both observations and variables and avoids the side effects of redundancy. An application to market segmentation is then briefly described to validate the learning algorithm introduced; identified clusters of products and motivations are used to simplify the analysis of the consumer segmentation by giving the user a first rough description of the different groups.
μ-SOM : Weighting features during clustering