Séminaire Quaresmi ce 19 novembre 2020 « Clustinity : community detection and clustering on complex data, application in a real study case on job matching in the human resources sector »

Jeudi 19 novembre à 14h, Etienne Cuvelier nous présentera un exposé intitulé « Clustinity : community detection and clustering on complex data, application in a real study case on job matching in the human resources sector » dont voici un résumé:

The vast majority of classification methods are designed for a given type
of variable.
However, describing real situations often requires mixing different types
such as numerical, categorical, interval or other values.
In this paper, we propose a method for merging similarity matrix
using quasi-arithmetic means allowing to mix the various dimensions of the data.
This method has been applied in the Human Resources sector.
The search for the job-candidate matching is an important task, both for
recruiting companies and for candidates looking for a job, but before
considering this matching, a segmentation of the offers on the basis of
the different skills and the geographical locations of the recruiters is required.
The experiment was carried out on a sample of 1760 offers extracted from
an original database of job offers.
Various parameterizations were tested with spectral clustering to
analyze these offers. We analyze the results obtained in this paper.

La présentation aura lieu sur Teams. Pour faciliter l’organisation, merci de vous inscrire auprès de Celine.Engelbeen@ichec.be.

Au plaisir de vous y voir nombreux !