Visualizing customer segmentations produce by self organizing maps (case study)
VIS '97 Proceedings of the 8th conference on Visualization '97
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The amount of financial information in today''s sophisticated large data bases is huge and makes comparisons between company performance - especially over time - difficult or at least very time consuming. The aim of this paper is to invest igate whether neural networks in the form of self-organizing maps can be used to manage the complexity in large data bases. We structure and analyze accoun ting numbers in a large data base over several time periods. By using self organizing maps, we overcome the problems associated with finding the appropriate und erlying distribution and the functional form of the underlying data in the structuring task that is often encountered, for example, when using cluster analysis. The method chosen also offers a way of visualizing the results. The data base in this study consists of annual reports of more than 120 world wide forest companies with data from a five year time period. This paper is an extended version of our paper Data Mining Accambis Numbers Using Self Organising Maps presented at Finnish Artificial Intelligenc e Conference in Vasa 20-23 August 1996.