The demand for talent has increased while the offer has declined and these worrying trends don’t seem to show any sign of change in the near future. According to Bloomberg Businessweek, USA, Canada, UK, and Japan (among many others) will face varying degrees of talent shortages in almost every industry in the coming years. The performed study focuses on identifying patterns which relates to human skills. Recently, with the new demand and increasing visibility, human resources are seeking a more strategic role by harnessing data mining methods. This can be achieved by discovering generated patterns from existing useful data in HR databases. The main objective of the paper is to determine which data mining algorithm suits best for extracting knowledge from human resource data, when in it comes to determining how suited is a candidate for a specific job. First of all, it must be determined a way to evaluate a candidate as objective as possible and rate the candidate with a mark from 0 to 10. To do so, some data sets had to be generated with different numbers of values or different values and wore processed using Weka. The results had been plotted so that it would be easier to interpret. Also, the study shows the importance of using large volumes of data in order to take informed decisions has recently become extremely discussed in most organizations. While finances, marketing and other departments within a company receive data systems and customized analysis, human resources are still not supported by expert systems to process large data volumes. The software prototype designed for the experiment rates individuals (working for the company, or in trials) on a scale from 0 to 10, offering the decision makers an objective analysis. This way, a company looking for talent will know whether the person applying for the job is suited or not, and how much the hiring will influence the overall rating of the department.
ADP Research Institute: Harnessing Big Data: The Human Capital Management Journey to Achieving Business Growth – ADP Global Human Capital Management Decision Makers Survey, 2015.
Bloomberg Businessweek, September 13 – September 19, 2010 issue, 54.
Chien, C.F., & Chen, L.F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems and Applications, 34(1), 380-290.
Chen, K.K., Chen, M.Y., Wu, H.J., & Lee, Y.L. (2007). Constructing a Web-based Employee Training Expert System with Data Mining Approach. Paper presented at the Paper in The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and Eservices (CEC-EEE 2007).
Elizabeth G. Chambers, Mark Fouldon, Helen Handfield-Jones, Steven M. Hankin, Edward G. Michaels III 1998, The War for Talent.
Han, J. & Kamber, M. (2001). Data mining: concepts and techniques (Morgan-Kaufman Series of Data Management Systems), Academic Press, San Diego.
Huang, M.J., Tsou, Y.L., & Lee, S.C. (2006). Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 19(6), 396-403.
Ranjan, J. (2008). Data Mining Techniques for better decisions in Human Resource Management Systems. International Journal of Business Information Systems, 3(5), 464-481.
Tung, K.Y., Huang, I.C., Chen, S.L., & Shih, C.T. (2005). Mining the Generation Xer’s job attitudes by artificial neural network and decision tree - empirical evidence in Taiwan. Expert Systems and Applications, 29(4), 783-794.
Tso, G.K.F., & Yau, K.K.W. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32, 1761-1768.
Zhao, X. (2008). An Empirical Study of Data Mining in Performance Evaluation of HRM. Paper presented at the International Symposium on Intelligent Information Technology Application Workshops, Hangzhou, China.