Skills and Vacancy Analysis with Data Mining Techniques

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Skills and Vacancy Analysis with Data Mining Techniques

Journal Title: Informatics - Year 2015, Vol 2, Issue 4

Abstract

Through recognizing the importance of a qualified workforce, skills research has become one of the focal points in economics, sociology, and education. Great effort is dedicated to analyzing labor demand and supply, and actions are taken at many levels to match one with the other. In this work we concentrate on skills needs, a dynamic variable dependent on many aspects such as geography, time, or the type of industry. Historically, skills in demand were easy to evaluate since transitions in that area were fairly slow, gradual, and easy to adjust to. In contrast, current changes are occurring rapidly and might take an unexpected turn. Therefore, we introduce a relatively simple yet effective method of monitoring skills needs straight from the source—as expressed by potential employers in their job advertisements. We employ open source tools such as RapidMiner and R as well as easily accessible online vacancy data. We demonstrate selected techniques, namely classification with k-NN and information extraction from a textual dataset, to determine effective ways of discovering knowledge from a given collection of vacancies.

Authors and Affiliations

Izabela A. Wowczko

Keywords

machine learning; text mining; k-NN; RapidMiner; R; skills; labor market

EP ID EP44047
DOI https://doi.org/10.3390/informatics2040031
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