How to use the Kohonen algorithm to simultaneously analyse individuals in a survey

The Kohonen algorithm (SOM, Kohonen,1984, 1995) is a very powerful tool for data analysis. It was originally designed to model organized connections between some biological neural networks. It was also immediately considered as a very good algorithm to realize vectorial quantization, and at the same time pertinent classification, with nice properties for visualization. If the individuals are described by quantitative variables (ratios, frequencies, measurements, amounts, etc.), the straightforward application of the original algorithm leads to build code vectors and to associate to each of them the class of all the individuals which are more similar to this code-vector than to the others. But, in case of individuals described by categorical (qualitative) variables having a finite number of modalities (like in a survey), it is necessary to define a specific algorithm. In this paper, we present a new algorithm inspired by the SOM algorithm, which provides a simultaneous classification of the individuals and of their modalities.Comment: Special issue ESANN 0

Advances in Self Organising Maps

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki University of Technology, in 2001 by the University of Lincolnshire and Humberside, and in 2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in Paris on September 5-8, 2005.Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Pari

NEURAL NETWORK AND SEGMENTED LABOUR MARKET

In France, for administrative reasons, unemployed workers may actually be involved in occasional work while remaining identified as unemployed (and receiving the corresponding benefit). This is due to the fact that the unemployed are deemed to be seeking full-time jobs and non-fixed term contracts of employment. This situation may be analysed as evidence of a special type of secondary segment of the labour market in a context of massive unemployment. The authors consider the effects of this situation both on the duration of unemployment and its recurrence may be usefully investigated.

Efficient estimators : the use of neural networks to construct pseudo panels

Pseudo panels constituted with repeated cross-sections are good substitutes to true panel data. But individuals grouped in a cohort are not the same for successive periods, and it results in a measurement error and inconsistent estimators. The solution is to constitute cohorts of large numbers of individuals but as homogeneous as possible. This paper explains a new way to do this: by using a self-organizing map, whose properties are well suited to achieve these objectives. It is applied to a set of Canadian surveys, in order to estimate income elasticities for 18 consumption functions..Pseudo panels ; self-organizing maps;

CLASSIFICATION OF RECURRING UNEMPLOYED WORKERS AND UNEMPLOYMENT EXITS

This study focuses on recurring unemployment, that is people with two or more spells of unemployment during the period of observation (July 1993 – August 1996). First, a classification is obtained which is then used to examine the specific role of occasional jobs during a spell of unemployment and, in this context, the influence of the received unemployment benefits on the duration of this spell. This paper is a continuation of previous analyses of unemployment in France, based on long-term data from the unemployed register held by ANPE (National Employment Bureau). The present analysis conducted using additional information about unemployment benefits received by the unemployed from UNEDIC (Unemployment Benefits Office).Unemployment, Labor Market, Kohonen Maps