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

Authors:
Sébastien Massoni;Madalina Olteanu;Patrick Rousset
Affiliations:
CES, Université Paris 1, Paris, France;SAMOS - CES, Université Paris 1, Paris, France;CEREQ, Marseille, France
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

Citing 4
Cited 1

Self-Organizing Maps

Self-Organizing Maps
Fast algorithm and implementation of dissimilarity self-organizing maps

Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Extracting Knowledge from Life Courses: Clustering and Visualization

DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Classifying qualitative time series with SOM: the typology of career paths in France

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks

Which dissimilarity is to be used when extracting typologies in sequence analysis? a comparative study

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.