Career-Path Analysis Using Optimal Matching and Self-Organizing Maps

Sébastien Massoni (1) , Madalina Olteanu (1, 2) , Patrick Rousset (3)

1 CES - Centre d'économie de la Sorbonne
2 SAMOS - Statistique Appliquée et MOdélisation Stochastique
3 CEREQ - Centre d'études et de recherches sur les qualifications

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.

Mots clés: Self-organizing maps; categorical data

Mots clés: clustering; career-paths

Domaines: Applications [stat.AP]

Sébastien Massoni, Madalina Olteanu, Patrick Rousset. Career-Path Analysis Using Optimal Matching and Self-Organizing Maps. José C. Principe, Risto Miikkulainen. Advances in Self-Organizing Maps, Springer, pp.154-162, 2009, Lecture Notes in Computer Science n°5629, ⟨10.1007/978-3-642-02397-2_18⟩. ⟨hal-00409114⟩