FUZZY SETS IN UNEMPLOYMENT PROBLEM
Abstract
Unemployment level moves in a cyclical manner. Hovewer, not only business cycle has an influence on unemployment levels, but also labour market policies and demographic developments may also influence the short and long-term evolution. Some unemployed are willing and able to work for pay currently available to work, and have actively searched for work, though. Unlike them, there is a group of unemployed, which are not are willing and able to change their position and find a job, “unhopefully” unemployed. For economic and social governance is key to define a group of “unhopefully” and “hopefully” unemployed. The purpose of this study was to find a statistical methodology, which help to define the group of “hopefully” unemployed. The study was held on unemployment from Czech Republic during 2014.References
Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, NewYork, 1981.
Eurostat, Unemploument statistics: http://ec.europa.eu/eurostat/statistics-explained/index.php/Unemployment_statistics.
Makhalova, E., Pecáková, I. (2013). Application fuzzy decision trees for creditworthiness evaluation. In International Scientific Conference "Whither Our Economies". Vilnius, 2013 pp. 26--34, ISSN 2029-8501. URL: http://woe.mruni.eu/wp-content/uploads/2013/11/WOE-2013-ONLINE.pdf.
Makhalova, E., Cséfalvaiová, K., Pecáková, I. (2014). Estimating the Correct Number of Clusters for Overlapping Data. Forum Statisticum Slovacum. 2014, sv. 10/ 6: 90-96., ISSN 1336-7420 URL:http://ssds.sk/casopis/archiv/2014/fss0614.pdf.
Oliviera, J.W. (2007). Advances in Fuzzy Clustering and its Applications, John Wiley & Sons, London, 2007.
Pharr, J. (2012). The Impact of Unemployment on Mental and Physical Health, Access to Health Care and Health Risk Behaviors. In International Scholarly Research Network ISRN Public Health, 2012.
Quinlan, J. R.,.(1986). Induction on decision trees, in Machine Learning, 1986, . 1:81–106.
Řezanková, H, Löster, T. (2011). Analysis of the Dependence of the Housing Characteristics on the Household Type in the Czech Republic. In: Aplimat [CD-ROM]. Bratislava, 01.02.2011 – 04.02.2011. Bratislava : Slovak University of Technology, 2011, pp. 1629–1636,ISBN 978-80-89313-52-5.
Riberiro, R. A. (1999). Soft computing in financial engineering, Heidelbarg ,1999, pp. 3-10.
Wanga, W., Zhang, Y. (2007). On fuzzy cluster validity indices, in Fuzzy sets and systems. 2007, 158:. 2095 – 211.
Xie, X. L., Beni, G., . (1991). Avalidity measure for fuzzy clustering, ieee trans. In Pattern anal. Mach. Intellegence, 1991, 13: 841–847.
Zadeh, L.A. (1975). Fuzzy logic and approximate reasoning.In Synthese ,1975, 407-428. 1975
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