Self-Organizing Maps
Career-Path Analysis Using Optimal Matching and Self-Organizing Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
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The purpose of this paper is to present a typology of career paths in France with the Kohonen algorithm and its generalization to a clustering method of life history using Self Organizing Maps. Several methods have already been proposed to transform qualitative information into quantitative one such as being able to apply clustering algorithm based on the Euclidean distance such as SOM. In the case of life history, these methods generally ignore the longitudinal organization of the data. Our approach aims to deduce a quantitative encode from the labor market situation proximities across time. Using SOM, the topology preservation is also helpful to check when the new encoding keep particularities of the life history and our economic approach of careers. In final, this quantitative encoding can be easily generalized to a method of clustering life history and complete the set of methods generalizing the use of SOM to qualitative data.