Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02397-2_18 | WSOM |
Field | DocType | Volume |
Hierarchical clustering,Employability,Drawback,Optimal matching,Categorical variable,Computer science,Self-organizing map,Theoretical computer science,Path analysis (statistics),Artificial intelligence,Limiting | Conference | 5629 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.42 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sébastien Massoni | 1 | 3 | 0.76 |
| Madalina Olteanu | 2 | 68 | 10.50 |
| Patrick Rousset | 3 | 17 | 2.67 |